qCT-Lung: Catching lung cancer early

In this blog, we will unbox qCT-Lung – our latest AI powered product that analyses Chest CT scans for lung cancer. At, we have always taken a holistic approach towards building solutions for lung health. qXR provides automated interpretation of chest X-rays and is complemented by qTrack, a disease & care pathway management platform with AI at its core. qCT-Lung augments our lung health suite with the ability to detect lung nodules & emphysema on chest CTs and analyze their malignancy. It can quantify & track nodules over subsequent scans. qCT-Lung is a CE certified product.

qCT-Lung banner

qCT-Lung: Catching lung cancer early


Medical Imaging has seen the biggest healthcare advancements in artificial intelligence (AI) and lung health has been at the forefront of these improvements. Lung health has also been a key domain of our product portfolio. We’ve built AI algorithms like qXR, which provides automated interpretation of chest X-rays. We augmented its capabilities with qTrack – our AI powered disease management platform, which solves for active case finding & tracking patients in care pathways. These applications have empowered healthcare practitioners at all stages of the patient journey in TB, Covid-19 & lung cancer screenings.

We’re adding a new member to our lung health suite: qCT-Lung. Its AI-powered algorithms can interpret chest CTs for findings like lung nodules & emphysema, and analyze their malignancy. It empowers clinicians to detect lung cancer in both screening programs as well as opportunistic screening settings.

qXR & qCT-Lung’s abilities to support clinicians with detection of lung cancer on chest X-rays & CTs complement qTrack’s disease management & patient tracking capability. Together, they round up our lung health portfolio to make it a comprehensive, powerful & unique offering.

Lung Cancer – The most fatal cancer

Lung cancer is the second most common cancer in both men & women. 2.2 million people were diagnosed with lung cancer worldwide in 2020 [1]. With 1.74 million deaths in 2020, lung cancer is also the leading cause of cancer related deaths (18.4%) resulting in more deaths than the second and third deadliest cancers combined (colorectal – 9.2% & stomach – 8.2%).

Future projections don’t look good either. Lung cancer incidents are projected to rise by 38% and the mortality is projected to rise by 39% by 2030 [2].

There are two main types of lung cancer:

  • Non-small cell lung cancer (NSCLC): NSCLC comprises of 80-85% of all lung cancer cases. Their major subtypes are adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. They are grouped together because of shared similarity in treatment & prognoses.
  • Small cell lung cancer (SCLC): SCLC tends to grow and spread faster than NSCLC. 10-15% of all lung cancers are SCLC.

There are also cancers that start in other organs (like breast) and spread to lung, but they don’t come under the vicinity of lung cancer.

Early detection & outcomes

Survival rates

The 5-year survival is a measure of what percent of people live at least 5 years after the cancer is found. The 5-year survival rates for both NSCLC & SCLC look as follows [4]:

Lung Cancer Survival rates

Lung Cancer Survival rates

The data shows that lung cancer mortality can be reduced significantly if detected & treated early.

Early detection

Data from England shows that the chances of surviving for at least a year decrease from 90% to 20% for the earliest to most advanced stage of lung cancer [5]. WHO elaborates on two components for early detection [6]:

Early diagnosis

Early identification of cancer results in better response to treatment, greater chances of survival, lesser morbidity & less expensive treatment. It comprises of 3 components:

  • Being aware of early symptoms of lung cancer like persistent cough, coughing up blood, pain in breathing, continuous breathlessness, loss of appetite, unexplained weight loss, etc [7].
  • access to clinical evaluation and diagnostic services
  • timely referral to treatment services.


Screening is aimed at identifying individuals with findings suggestive of lung cancer before they have developed symptoms. Further tests are conducted to establish if the diagnosis should be followed or referral for treatments should be made. They’re effective because symptoms of lung cancer do not appear until the disease is already at an advanced stage.

Lung Cancer Screening Programs

Screening programs use regular chest X-rays and low dose CT/ CAT scans to study people at higher risk of getting lung cancer. CT scans have proven to be more effective than X-rays. They resulted in a 20% reduction in lung cancer-specific deaths as compared to X-rays [2]. However, X-rays are more accessible and cheaper and thus, are important for low-income settings.

The U.S. Preventive Services Task Force (USPSTF) recommends yearly lung cancer screening with LDCT for people who [9]:

  • Have a 20 pack-year or more smoking history, and
  • Smoke now or have quit within the past 15 years, and
  • Are between 50 and 80 years of age.

Challenges in radiology screening today

Chest CTs are comparatively more accurate than chest X-rays for identification of thoracic abnormalities. This is because of lack of superimposition, greater contrast, and spatial resolution. However, there are many challenges in identifying & reporting lung cancer on Chest CTs. These challenges can be divided into the following categories:


A study revealed that 42.5% of malpractice suits on radiologists are because of failure to diagnose lung cancer [14]. These lawsuits can cost as high as $10M [15]. Misdiagnosis can occur due to two reasons [11]:

  • Lesion characteristics: Small dimension, poor conspicuousness , ill-defined margins and central location are the most common lesion characteristics that lead to missed lung cancers incidences.
  • Observer Error: There are multiple sources of observer error like:
    • Recognition error consists of missed detection of lesions.
    • Decision making error includes cases of inaccurately interpreted characteristics of a detected malignant lesion as benign/ normal.
    • Satisfaction of search error occurs when the observer fails to continue to search for subsequent abnormalities after identifying an initial one. Typically, this happens due to two possible mechanisms: ceasing the search for other abnormalities early in a positive exam and focusing on the wrong part of the exam.

Analysis & Tracking

Post detection of a lesion, a major challenge is to analyse its characteristics and determine malignancy. Even when the lesion’s malignancy is determined correctly, tracking them over subsequent scans is challenging for screening programs due to lack of appropriate CADs & tools.

Structured reporting & Follow-ups

Structured reporting helps to categorize results and recommend follow-ups based on chances of malignancy by considering size, appearance, and growth of the lesion. Further, volume measurement & volume doubling times (VDT) have been proposed in the management protocol of NELSON lung cancer screening trial [13]. All these metrics are challenging to calculate & report in absence of appropriate tools. This makes it hard to standardize follow up recommendations based on guidelines like Fleishner Society or Lung-RADS scores.

Detecting relevant co-findings

Certain other pulmonary findings like COPD (chronic obstructive pulmonary disease) are an independent risk factor for lung cancer. Lung cancer screening subjects have a high prevalence of COPD which accounts for significant morbidity and mortality.
One of the major benefits of emphysema (a type of COPD) quantification in lung cancer screening patients is an earlier diagnosis and therapy of COPD with smoking cessation strategies. It can potentially lead to less COPD-related hospitalizations.

Time constraints

Interpreting CT scans is a time intensive process. A CT scan can have 16 to 320 slices compared to one or two images in an X-ray. Radiologists spend 5-10 minutes to interpret & report each CT scan.

For chest CTs, detecting small nodules through hundreds of slices consumes a lot of time. There are tools that help with some of these issues but none of them solve for lung cancer screening comprehensively.

qCT-Lung: AI powered lung nodule interpretation tool

qCT-Lung empowers lung cancer screening programs and facilitates opportunistic screening by detecting malignant lesions using AI. It is aimed at helping clinicians with all the issues discussed in the previous section – misdiagnosis, analysis, reporting, detection of co-findings & reducing time constraints. The algorithm is trained on more than 200k chest CTs and can detect, analyze, monitor and auto-report lung nodules.
This is how qCT-Lung assists clinicians in interpreting chest CTs for lung nodules:

Detecting & Quantifying Lesions

Secondary Capture

Secondary Capture with detected nodule

qCT can distinguish lung lesions from complex anatomical structures on lung CTs and minimize instances of letting lung cancers go undetected, by preventing nodules from being overlooked on scans. Faster and more accurate detection helps decrease time to treatment and improves patient outcomes.


  • Detects lung nodules as small as 3mm with high accuracy (sensitivity of 95% and less than 1 false positives per scan)
  • Detects emphysema
  • Reduces the chance of missed nodules
  • Auto quantification of diameter & volume

Analysis & Growth Monitoring

Nodule Analysis & Malignancy Risk

Nodule Analysis & Malignancy Risk

qCT analyzes nodule characteristics to determine malignancy. The algorithm also assigns a malignancy risk score for each of the nodules that helps clinicians plan treatments.


  • Analyses localization, spiculation, size, calcification & texture (solid, sub-solid & ground glass nodules)
  • Calculates Malignancy Risk Score
  • Measures volumetry and tracks growth of nodules
  • Predicts nodule volume doubling time
  • Precisely quantifies response to treatment

Reporting Assistance

Pre-filled report with suggested follow-ups

Pre-filled report with suggested follow-ups

qCT-Lung utilizes pre-populated results to offer clinicians faster reporting, that reduces time to treatment and further diagnosis. It can also recommend timelines for follow-up scans.


  • Automates reporting to save time and reduce reporting workload
  • Pre-fed with the Lung-RADS & Fleischer Society Guidelines to suggest follow-ups.

Modifiable Results

qCt-Lung also offers a lung nodule reporting platform that is designed for screening programs. It enables clinicians to choose which nodules to include in the report and also to add new nodules. The platform pre-populates the image viewer with nodules identified by qCT-Lung. Clinicians can then exclude or add new nodules to this list. The final list after these changes is sent to the RIS.


The platform empowers physicians to modify the results generated by qCT-Lung and report on what’s profoundly important for them.

Qure’s Lung Health suite: A 3–pronged approach

Qure's Lung Health Suite

We have built an end-to-end portfolio for managing lung cancer screenings in all kinds of resource-settings. Lung cancer screening has many challenges. While CTs are recommended imaging modality, resource limited settings must depend on X-rays for its cost benefit and easy availability. Patient tracking, disease management and long term follow up for individuals with high-risk cases are also a challenge. Our comprehensive lung health suite takes care of these challenges.

  1. qXR – our chest X-ray interpretation algorithm detects lung nodules on X-rays with high accuracy.
  2. qCT-lungs does the same on chest CTs.
  3. qTrack is built and designed for community screening to track an individual’s disease and manage care pathways.

Together, these solutions can help in active case screening, monitoring disease progression, reducing turn-around-time, linking care to treatment, & improving care pathways.

Write to us at to integrate qCT-Lung in your lung nodule management pathway.


  1. Key Statistics for Lung Cancer
  2. Lung Cancer Fact Sheet
  3. What Is Lung Cancer?
  4. Lung Cancer Survival Rates
  5. Cancer Research UK: Why is early diagnosis important?
  6. WHO: Fact Sheet on Cancer
  7. NHS UK: Lung Cancer Symptoms
  8. Can Lung Cancer Be Found Early?
  9. CDC: Who Should Be Screened for Lung Cancer?
  10. National Lung Screening Trial Research Team, Aberle DR, Berg CD, et al. “The National Lung Screening Trial: overview and study design.” Radiology. 2011;258(1):243–253.
  11. del Ciello A, et al. “Missed lung cancer: when, where, and why? Diagnos.” Intervent. Radiol. 2017;23:118–126. doi: 10.5152/dir.2016.16187.
  12. Widmann, G. “Challenges in implementation of lung cancer screening—radiology requirements.” memo 12, 166–170 (2019).
  13. Dong Ming Xu, Hester Gietema, Harry de Koning, René Vernhout, Kristiaan Nackaerts, Mathias Prokop, Carla Weenink, Jan-Willem Lammers, Harry Groen, Matthijs Oudkerk, Rob van Klaveren, “Nodule management protocol of the NELSON randomised lung cancer screening trial”, Lung Cancer, Volume 54, Issue 2, 2006, Pages 177-184, ISSN 0169-5002
  14. Baker SR, Patel RH, Yang L, Lelkes VM, Castro A 3rd. “Malpractice suits in chest radiology: an evaluation of the histories of 8265 radiologists.” J Thorac Imaging. 2013 Nov;28(6):388-91.
  15. HealthImaging: Lung cancer missed on CT prompts $10M lawsuit against U.S. government


Time is Brain: AI helps cut down stroke diagnosis time in the Himalayan foothills

Stroke is a leading cause of death. Stroke care is limited by the availability of specialized medical professionals. In this post, we describe a physician-led stroke unit model established at Baptist Christian Hospital (BCH) in Assam, India. Enabled with qER, Qure’s AI driven automated CT Brain interpretation tool, BCH can quickly and easily determine next steps in terms of treatment and examine the implications for clinical outcomes.

qER at a Stroke unit

Across the world, Stroke is a leading cause of death, second only to ischemic heart disease. According to the the World Stroke Organization (WSO), 13.7 million new strokes occur each year and there are about 80 million stroke survivors globally. In India as per the Health of the Nation’s State Report we see an incidence rate of 119 to 152/100000, and has a case fatality rate of 19 to 42% across the country.

Catering to tea plantation workers in and around the town of Tezpur, the Baptist Christian Hospital, Tezpur (BCH) is a 130-bed secondary care hospital in the North eastern state of Assam in India. This hospital is a unit of the Emmanuel Hospital Association, New Delhi. From humble beginnings, offering basic dispensary services, the hospital grew to become one of the best healthcare providers in Assam, being heavily involved in academic and research work at both national and international levels.

Nestled below the Himalayas, interspersed with large tea plantations, Assamese indigenous population and tea garden workers showcase a prevalence of hypertension, the largest single risk factor of stroke, reportedly between 33% to 60.8%. Anecdotal reports and hospital-based studies indicate a huge burden of stroke in Assam – a significant portion of which is addressed by Baptist Hospital. Recent study showed that hemorrhagic strokes account for close to 50% of the cases here, compared to only about 20% of the strokes in the rest of India.

Baptist Christian Hospital

Baptist Christian Hospital, Tezpur. Source

Challenges in Stroke Care

One of the biggest obstacles in Stroke Care is the lack of awareness of stroke symptoms and the late arrival of the patient, often at smaller peripheral hospitals, which are not equipped with the necessary scanning facilities and the specialists, leading to a delay in effective treatment.

The doctors and nurses of the Stroke Unit at BCH, Tezpur were trained online by specialist neurologists, who in turn trained the rest of the team on a protocol that included Stroke Clinical Assessment, monitoring of risk factors and vital parameters, and other supportive measures like management of Swallow assessment in addition to starting the rehabilitation process and advising on long term care at home. A study done at Tezpur indicated that post establishment of Stroke Unit, there was significant improvement in the quality of life along with reduction in deaths compared to the pre-Stroke Unit phase.

This is a crucial development in Stroke care especially in the low and middle income countries(LMIC) like India, to strengthen the peripheral smaller hospitals which lack specialists and are almost always the first stop for patients in emergencies like Stroke.

Stroke pathway barriers

This representative image details the acute stroke care pathway. Source

The guidelines for management of acute ischemic stroke involves capturing a non-contrast CT (NCCT) study of the brain along with CT or MRI angiography and perfusion and thrombolysis-administration of rTPA (Tissue Plasminogen Activator) within 4.5 hours of symptom onset. Equipped with a CT machine and teleradiology reporting, the physicians at BCH provide primary intervention for these stroke cases after a basic NCCT and may refer them to a tertiary facility, as applicable. They follow a Telestroke model-in cases where thrombolysis is required, the ER doctors consult with neurologists at a more specialized center and the decision making is done upon sharing these NCCT images via phone-based mediums like WhatsApp while severe cases of head trauma are referred for further management to far away tertiary facilities. There have been studies done on a Physician based Stroke Unit model in Tezpur, that has shown an improvement in treatment outcomes.

How is helping BCH with stroke management?

BCH and Qure have worked closely since the onset of the COVID-19 pandemic, especially at a time when confirmatory RT-PCR kits were limiting. qXR, Qure’s AI aided chest X-ray solution had proved to be a beneficial addition for identification of especially asymptomatic COVID-19 suspects and their treatment and management, beyond its role in comprehensive chest screening.

qER messages


In efforts to improve the workflow of stroke management and care at the Baptist hospital, qER, FDA approved and CE certified software which can detect 12 abnormalities was deployed. The abnormalities including five types of Intracranial Hemorrhages, Cranial Fractures, Mass effect, midline Shift, Infarcts, Hydrocephalus, Atrophy etc in less than 1-2 minutes of the CT being taken. qER has been trained on CT scans from more than 22 different CT machine models, thus making it hardware agnostic. In addition to offering a pre-populated radiology report, the HIPAA compliant qER solution is also able to label and annotate the abnormalities in the key slices.

Since qER integrates seamlessly with the existing technical framework of the site, the deployment of the software was completed in less an hour along with setting up a messaging group for the site. Soon after, within minutes of taking the Head CT, qER analyses were available in the PACS worklist along with messaging alerts for the physicians’ and medical team’s review on their mobile phones.

The aim of this pilot project was to evaluate how qER could add value to a secondary care center where the responsibility for determination of medical intervention falls on the physicians based on teleradiology report available to them in a span of 15-60 minutes. As is established with stroke care, every minute saved is precious.

Baptist Christian Hospital

Physician using qER

At the outset, there were apprehensions amongst the medical team about the performance of the software and its efficacy in improving the workflow, however, this is what they have to say about qER after 2 months of operation:

“qER is good as it alerts the physicians in a busy casualty room even without having to open the workstation. We know if there are any critical issues with the patient” – Dr. Jemin Webster, a physician at Tezpur.

He goes on to explain how qER helps grab the attention of the emergency room doctors and nurses to critical cases that need intervention, or in some instances, referral. It helps in boosting the confidence of the treating doctors in making the right judgement in the clinical decision-making process. It also helps in seeking the teleradiology support’s attention into the notified critical scans, as well as the scans of the stroke cases that are in the window period for thrombolysis. Dr. Jemin also sees the potential of qER in the workflow of high volume, multi-specialty referral centers, where coordination between multiple departments are required.

The Way Ahead

A technology solution like qER can reduce the time to diagnosis in case of emergencies like Stroke or trauma and boosts the confidence of Stroke Unit, even in the absence of specialists. The qER platform can help Stroke neurologists in the Telestroke settings access great quality scans even on their smartphones and guide the treating doctors for thrombolysis and further management. Scaling up this technology to Stroke units and MSUs can empower peripheral hospitals to manage acute Stroke especially in LMICs.

We intend to conduct an observational time-motion study to analyze the Door-to- Needle time with qER intervention via instant reports and phone alerts as we work through the required approvals. Also in the pipeline is performance comparison of qER reporting against the Radiologist report as ground truth along with comparison of clinical outcomes and these parameters before and after introduction of qER into the workflow. We also plan to extend the pilot project to Padhar Mission Hospital, MP and the Shanthibhavan Medical Center, Simdega, Jharkhand.

Qure team is also working on creating a comprehensive stroke platform which is aimed at improving stroke workflows in LMICs and low-resource settings.


qScout – Strengthening Global Vaccine Programs

Why an agile monitoring and management system is the need of the hour

The world is seeing unprecedented times. Two relentless years of a pandemic is enough to break even the strongest healthcare systems, that along with the current efforts to ramp up vaccinations and clinical care has resulted in most countries public health system being strained or barely functional. Such accelerated development and roll out of multiple vaccines, is a first for any disease.It is important that each of these vaccines and its effects are monitored for substantial periods of time to understand short term and long-term effects on varying demographic and risk factor profiles of vaccine recipients.

What is Active Vaccine Safety Surveillance (AVSS)?

The traditional surveillance systems in most countries, rely heavily on health care providers to notify the adverse events. This is a passive surveillance system that helps in detecting unsolicited adverse events Vaccine Survey is another conventional method, but the disadvantage is that it usually is a cross sectional survey with only a one time follow up. One of the ways to augment these traditionalsurveillance systems is to empower the vaccine recipient using smartphone based digital tools. AVSS or Active Vaccine Safety Surveillance systems helps by proactively enrolling many vaccine recipients who are followed up for all minor and major adverse events. This can significantly alleviate the burdens off frontline workers, while capturing large amount of data, frequently and in a timely fashion. Besides allowing the healthcare systems to address immediate or delayed adverse events, this has the potential to monitor the health of the community in the long term as well.

Policy formulation has also been extremely difficult for governments and world organisations given the novelty of the disease. This solution could allow for faster data driven decision making empowering governments and policy makers in a way that only technology can.

Need for AVSS in Covid-19

Post Vaccine monitoring: Before COVID–19, vaccines used to be licensed after 4-15years of rigorous clinical trials. With the fast-tracked development of COVID-19 vaccines, there is a likelihood that some of the rare and long-term adverse events may have gone undetected in the clinical trials. Through AVSS via phone, the vaccine recipients can be monitored for a period ranging from 07 days up to 12 months, getting real time alerts for any serious adverse events following immunization (AEFI)or Adverse Events of Special Interest (AESI). By automating this process, we have successfully tracked the symptoms fast enough to be of actionable value; the healthcare worker getting involved only if necessary.

Large Data collection and Analysis: we need interoperable systems that can harmonise data from multiple sites, with a validated AI algorithm to measure the risk of AEFIs and their early indicators. The system will need to be agile and scalableto work in varying resource settings.

Country-level Surveillance: There must be a centralised dashboard for policy makersand regulatory authorities to visualize community vaccine uptake statistics, AEFI patterns and efficacies.

qScout for AVSS monitoring

qScout is’s Artificial Intelligence and NLP-powered solution that improves vaccine recipient’s experience while augmenting traditional surveillance systems forindividual’s health monitoring. It has a smartphone-based component for easy interaction between the recipient and public health professionals.

How can qScout be used for active surveillance and monitoring of vaccinees?

Step 1: Walk-in/registered individuals at COVID-19 vaccination sites will be enrolled using qScout EMR by recording the following details :

  • Personal Identifiers
  • Risk groups
  • Medication history
  • Name and details of the vaccine administered.

QScout monitor demo

Step 2: Once the enrollment is completed, the vaccinated person receives a message on the mobile for their consent. Follow-up messages will be sent for a set period to check for any adverse/unexpected symptoms (AEFIs or AESI). The person will also be reminded about the second dose. Every enrolled individual will be monitored for a predefined period , as per the guidelines of the proposed project.

Step 3: Public health officials who have access to the data can see the analysis of the AEFIs OR AESIs on a real time dashboard The information will be segregated based on demographics, type of vaccine administered, count of individuals administered with dose 1 and/or dose 2 as well as percent drop-out between both the doses.

Benefits of Real-time remote patient monitoring after vaccination

  1. Early and timely detection and notification of serious AEFIs or AESIs
  2. Detection of rare and unknown adverse events, that may have not been detected during the clinical trials
  3. Recipient risk profiling and Predictive adverse events scoring or modelling
  4. Long term adverse effects of vaccination
  5. Identifying re-infection probabilities and severity
  6. Monitoring of Vaccine administration SOP adherence and Pharmacovigilance/ Post Market Surveillance for vaccine manufacturer

Prior Experience:

Country wide contact tracing and remote patient management: A Case Study

During the first wave of the COVID-19 pandemic, the qScout platform was adopted for national contact tracing and management mechanism by Ministry of Health in Oman. Within a span of a few weeks, qScout was integrated with Tarassud plus, the country’s ICT platform for surveillance and monitoring. qScout used AI chatbot customised to the local languages and engaged with confirmed cases capturing their primary and secondary symptoms. The AI engine analysed the information and provided insights enabling virtual triaging and timely escalation for medical requirements. Over a span of 8 moths, approximately 400,000 Covid-19 patients under quarantine in Oman regularly interacted with a software for over thousands of sessions taking off a significant proportion of healthcare workers’ burden. All this while the health authorities and government actively kept a watch centrally to monitor hotspot regions, areas needing additional resources and so on. Having qScout enabled with multi-lingual support in English as well as Arabic helped increase the ease of interaction for various users.

The software was deployed with a gadget that relayed instant reports to the competent authorities about the movements and locations that a quarantined or infected person visits. It also had the capability to send alerts if this person left their location or tried taking it off. This level of data collection allowed sharing relevant insights with the Ministry of Health about population level statistics vital for planning for resources. This coupled with’s qSCOUT, was a true exemplar of use of technology to tackle the pandemic.

Way Forward:

There are multiple studies that are ongoing with regional and state governments as well as non-governmental organizations. qScout is designed as a platform for monitoring safety and efficacy of all adult and pediatric vaccines and medications.


Engineering Radiology AI for National Scale in the US

vRad, a large US teleradiology practice and have been colloborating for more than an year for a large scale radiology AI deployment. In this blog post, we describe the engineering that goes into scaling radiology AI. We discuss adapting AI for extreme data diversity, DICOM protocol and software engineering.

vRad and have been collaborating on a large-scale prospective validation of qER,’s ICH model for detecting intracranial hemorrhages (ICH) for more than a year. vRad is a large teleradiology practice – 500+ radiologists serving over 2,000 facilities in the United States – representing patients from nearly all states. vRad uses an in-house built RIS and PACS that processes over 1 million studies a month, with the majority of those studies being XR or CT. Of these, about 70,000 CT studies a month get processed by’s algorithms. This collaboration has produced interesting insights into the challenges of implementing AI on such a large scale. Our earlier work together is published elsewhere at Imaging Wire and vRad’s blog.

Models that are accurate on extremely diverse data

Before we discuss the accuracy of models, we have to start with how we actually measure it at scale. In this respect, we have leveraged our experience from prior AI endeavors. vRad runs the imaging models during validation in parallel with production flows. As an imaging study is ingested into the PACS, it is sent directly to validation models for processing. In turn, as soon as the radiologist on the platform completes their report for the scan, we use it to establish the ground truth. We used our Natural Language Processing (NLP) algorithms to automatically read these reports to assign whether the current scan is positive or negative for ICH. Thus, the sensitivity and specificity of a model can be measured in real-time this way on real-world data.

AI models often perform well in the lab, but when tried in a real-world clinical workflow, it does not live up to expectations. This is a combination of problems. The idea of a diverse, heterogeneous cohort of patients is well discussed in the space of medical imaging. In this case,’s model was measured with a cohort of patients representative of the entire US population – with studies from all 50 states flowing through the model and being reported against.

Less commonly discussed are the challenges with the uniqueness of data that is a hospital or even imaging device-specific. vRad receives images from over 150,000 unique imaging devices in over 2,000 facilities. At a study level, different facilities can have many different study protocols – varying amounts of contrast, varying radiation dosages, varying slice thicknesses, and other considerations can change how well a human radiologist can evaluate a study, let alone the AI model.

Just like human radiologists, AI models do their best if they see consistent images at pixel level despite the data diversity. Nobody would want to recalibrate their decision process just because different manufacturers chose to use different post-processing techniques. For example, image characteristics of a thin slice CT scan are quite different from a 5mm thick scan with the former being considerably noisier. Both AI and doctors are sure to be confused if asked to decide whether those subtle hyperdense dots that they see on a thin slice scan are just noise or symptoms of diffuse axonal injury. Therefore, we invested considerably in making sure the diverse data is pre-processed into highly consistent raw pixel data. We discuss more in the following section.

A thin slice CT (left) vs a thick slice one (right)

A thin slice CT (left) vs a thick slice one (right)

DICOM, AI, and interoperability

Dealing with patient and data diversity are major components of AI models. The AI model not only has to be generalizable at the pixel level, but it also must make sure the right pixels are fed into it. The first problem is highly documented in the AI literature but the second one, not so much. As traditional AI imaging models are trained to work on natural images (think cat photos), they deal with simplistic data formats like PNG or JPEG. However, medical imaging is highly structured and complex and contains orders more data compared to natural images. DICOM is the file format and standard used for storing and transfer the medical images.

While DICOM is a robust and well-adopted standard, implementation details vary. Often DICOM tags differ greatly from facility to facility, private tags vary from manufacturer to manufacturer, encodings and other imaging-device specific differences in DICOM require that any piece of software, including an AI model, be robust and good at error handling. After a decade of receiving DICOM from all over the U.S., the vRad PACS still runs into new unique configurations and implementations a few times a year, so we are uniquely sensitive to the challenges.

A taste of DICOM diversity: shown are random study descriptions used to represent CT brain

A taste of DICOM diversity: shown are random study descriptions used to represent CT brain

We realized that we need another machine learning model to solve this interoperability problem itself. How do we recognize that this particular CT image is not a brain image even if the description of images says so? How do we make sure the complete brain is present in the image before we decide there is a bleed in it? Variability of DICOM metadata doesn’t allow us to write simple rules which can work at scale. So, we have trained another AI model based on metadata and pixels which can make the above decisions for us.

These challenges harken back to classic healthcare interoperability problems. In a survey by Philips, the majority of younger healthcare professionals indicated that improved interoperability between software platforms and healthcare practices is important for their workplace satisfaction. Interestingly, these are the exact challenges medical imaging AI has to solve for it to work well. So, AI generalizability is just another name for healthcare interoperability. Given how we used machine learning and computer vision to solve the interoperability problems for our AI model, it might be that solving wider interoperability problems might involve AI itself.

AI Software Engineering

But even after those generalizability/interoperability challenges are overcome, a model must be hosted in some manner, often in a docker-based solution, frequently written in Python. And like the model, this wrapper must scale the solution. It must handle calls to the model and returning results, as well as logging information for the health of the system just like any other piece of software. As a model goes live on a platform like vRad’s, common problems that we see happen are memory overflows, underperforming throughput, and other “typical” software problems.

Although these problems look quite similar to traditional “software problems”, the root cause is quite different. For the scalability and the reliability of traditional software, the bottleneck usually boils down to database transactions. Take Slack, an enterprise messaging platform, for example. What’s the most compute-intensive thing Slack app does? It looks up the chat typed previously by your colleague from a database and shows it to you. Basically, a database transaction. The scalability of Slack usually means scalability and reliability of these database transactions. Given how databases have been around for years, this problem is fairly well solved with off-the-shelf solutions.

For an AI enabled software, the most compute intensive task is not a database transaction but running of an AI model. And this is arguably more intensive than a database lookup. Given how new deep learning is, the ecosystem around it is not yet well-developed. This make AI model deployment and engineering hard and it is being tackled by big names like Google (Tensorflow), Facebook (Torch), and Microsoft (ONNX). Because these are opensource, we actively contribute to them and make them better as we come across problems.

As different is the root cause of the engineering challenges, the process to tackle them is surprisingly similar. After all, engineers’ approach to building bridges and rockets is not all that different, they just require different tools. To make our AI scale to vRad, we followed traditional software engineering best practices including highly tested code and frequent updates. As soon as we identify an issue, we patch it up and write a regression test to make sure we never come across it again. Docker has made deployment and updates easy and consistent.

Automated slack alerts

We get automated alerts of the errors and fix them proactively

Integration to clinical workflow

Another significant engineering challenge we solved is to bend clinical software to our will. DICOM is a messy communication standard and lacks some important features. For example, DICOM features no acknowledgement signal that the complete study has been sent over the network. Another great example is the lack of standardization in how a given study is described – what fields are used and what phrases are used to describe what the study represents. The work and vRad collaborated on the required intelligent mapping of study descriptions and modality information throughout the platform – from the vRad PACS through the Inference Engine running the models to the actual logic in the model containers themselves.

Many AI image models and solutions on the market today integrate with PACS and Worklists, but one unique aspect of Qure.AI and vRad’s work is the sheer scale of the undertaking.  vRad’s PACS ingests millions of studies a year, around 1 billion individual images annually. The vRad platform, including the PACS, RIS, and AI Inference Engine, route those studies to the right AI models and the right radiologists, radiologists perform thousands of reads each night, and NLP helps them report and analyze those reports for continual feedback both to radiologists as well as AI models and monitoring.  Qure.AI’s ICH model plugged into the platform and demonstrated robustness as well as impressive sensitivity and specificity.

During vRad and’s validation, we were able to run hundreds of thousands of studies in parallel with our production workloads, validating that the model and the solution for hosting the model was able to not only generalize for sensitivity and specificity but overcome all of these other technical challenges that are often issues in large-scale deployments of AI solutions.


Smarter City: How AI is enabling Mumbai battle COVID-19

When the COVID-19 pandemic hit Mumbai, one of the most densely populated cities in the world, the Municipal Corporation of Greater Mumbai (MCGM) promptly embraced newer technologies, while creatively utilising available resources. Here is a deeper dive into how the versatility of chest x-rays and Artificial Intelligence helped the financial capital of India in efforts to containing this pandemic.

The COVID-19 pandemic is one of the most demanding adversities that the present generation has had to witness and endure. The highly virulent novel Coronavirus has posed a challenge like no other to the most sophisticated healthcare systems world over. Given the brisk transmission, it was only a matter of time that the virus spread to Mumbai, the busiest city of India, with a population more than 1.5 times that of New York.

The resilient Municipal Corporation of Greater Mumbai (MCGM), swiftly sprang into action, devising multiple strategies to test, isolate, and treat in an attempt to contain the pandemic and avoid significant damage. Given the availability and effectiveness of chest x-rays, they were identified to be an excellent tool to rule-in cases that needed further testing to ensure that no suspected case was missed out. Though Mumbai saw a steep rise in cases more than any other city in India, MCGM’s efforts across various touchpoints in the city were augmented using Qure’s AI-based X-ray interpretation tool – qXR – and the extension of its capabilities and benefits.

In the latter half of June, MCGM launched the MISSION ZERO initiative, a public-private partnership supported by the Bill & Melinda Gates Foundation, Bharatiya Jain Sanghatana (BJS) and Desh Apnayen and CREDAI-MCHI. Mobile vans with qXR installed digital X-ray systems were stationed outside various quarantine centers in the city. Individuals identified to be at high-risk of COVID-19 infection by on-site physicians from various camps were directed to these vans for further examination. Based on the clinical and radiological indications of the individuals thus screened, they were requested to proceed for isolation, RT-PCR testing, or continue isolation in the quarantine facility. Our objective was to reduce the load on the centers by continuously monitoring patients and discharging those who had recovered, making room for new patients to be admitted, and ensuring optimal utilization of resources.

A patient being screen in a BJS van equipped with qXR

The approach adopted by MCGM was multi-pronged to ascertain that no step of the pandemic management process was overlooked:

  • Triaging of high-risk and vulnerable and increase in case-detection in a mass screening setting to contain community transmission (11.4% individuals screened)
  • Patient management in critical care units to manage mortality rates
  • Support the existing healthcare framework by launching MISSION ZERO initiative and using chest X-ray based screening for optimum utilization of beds at quarantine centers

Learn more about qXR COVID in our detailed blog here

Triaging and Improvement in Case Finding

Kasturba Hospital and HBT Trauma Center were among the first few COVID-19 testing centers in Mumbai. However, due to the overwhelming caseload, it was essential that they triage individuals flowing into fever clinics for optimal utilization of testing kits.  The two centers used conventional analog film-based X-ray machines, one for standard OPD setting and another portable system for COVID isolation wards

From early March, both these hospitals adopted

  1. qXR software – our AI-powered chest X-ray interpretation tool provided the COVID-19 risk score based on the condition of the patient’s lungs
  2. qTrack – our newly launched disease management platform

The qTrack mobile app is a simple, easy to use tool that interfaces qXR results with the user. The qTrack app digitizes film-based X-rays and provides real-time interpretation using deep learning models. The x-ray technician simply clicks a picture of the x-ray against a view box via the app to receive the AI reading corresponding to the x-ray uploaded. The app is a complete workflow management tool, with the provision to register patients and capture all relevant information along with the x-ray. The attending physicians and the hospital Deans were provided separate access to the Qure portal so that they could instantly access AI analyses of the x-rays from their respective sites, from the convenience of their desktops/mobile phones.

qXR app in action at Kasturba Hospital

qXR app in action at Kasturba Hospital

Triaging in Hotspots and Containment Zones

When the city went into lockdown along with the rest of the world as a measure to contain the spread of infection, social distancing guidelines were imposed across the globe. However, this is not a luxury that the second-most densely populated city in the world could always afford. It is not uncommon to have several families living in close quarters within various communities, easily making them high-risk areas and soon, containment zones. With more than 50% of the COVID-19 positive cases being asymptomatic cases, it was imperative to test aggressively. Especially in the densely populated areas to identify individuals who are at high-risk of infection so that they could be institutionally quarantined in order to prevent and contain community transmission.

Workflow for COVID-19 management in containment zones using qXR

Workflow for COVID-19 management in containment zones using qXR

The BMC van involved in mass screenings and qXR in action in the van

The BMC van involved in mass screenings and qXR in action in the van

Patient Management in Critical Care Units

As the global situation worsened, the commercial capital of the country saw a steady rise in the number of positive cases. MCGM, very creatively and promptly, revived the previously closed down hospitals and converted large open grounds in the city into dedicated COVID-19 centers in record time with their own critical patient units. The BKC MMRDA grounds, NESCO grounds, NSCI (National Sports Council of India) Dome, and SevenHills Hospital are a few such centers.


The COVID-19 center at NESCO is a 3000-bed facility with 100+ ICU beds, catering primarily to patients from Mumbai’s slums. With several critical patients admitted here, it was important for Dr. Neelam Andrade, the facility head, and her team to monitor patients closely, keep a check on their disease progression and ensure that they acted quickly.  qXR helped Dr. Andrade’s team by providing instant automated reporting of the chest X-rays. It also captured all clinical information, enabling the center to make their process completely paperless.

The patient summary screen of qXR web portal

The patient summary screen of qXR web portal

“Since the patients admitted here are confirmed cases, we take frequent X-rays to monitor their condition. qXR gives instant results and this has been very helpful for us to make decisions quickly for the patient on their treatment and management.”

– Dr Neelam Andrade, Dean, NESCO COVID centre

SevenHills Hospital, Andheri

Located in the heart of the city’s suburbs, SevenHills Hospital was one of the first hospitals that were revived by MCGM as a part of COVID-19 response measures.

The center played a critical role on two accounts:

  1. Because patients were referred to the hospital for RT-PCR testing from door-to-door screening by MCGM. If found positive, they were admitted at the center itself for quarantine and treatment.
  2. With close to 1000 beds dedicated to COVID-19 patients alone, the doctors needed assistance for easy management of critical patients and to monitor their cases closely.

As with all COVID-19 cases, chest x-rays were taken of the admitted patients periodically to ascertain their lung condition and monitor the progress of the disease. All x-rays were then read by the head radiologist, Dr. Bhujang Pai, the next day, and released to the patient only post his review and approval. This meant that on most mornings, Dr. Pai was tasked with reading and reporting 200-250 x-rays, if not more. This is where qXR simplified his work.

Initially, we deployed the software on one of the two chest X-ray systems. However, after stellar feedback from Dr. Pai, our technology was installed in both the machines. In this manner AI, pre-read was available for all chest X-rays of COVID-19 patients from the center.

Where qXR adds most value:

  • several crucial indications are reported up by qXR
  • percentage lung affected helps to quantify improvement/deterioration in the patient lung and provide an objective assessment of the patient’s condition
  • pre-filled PDF report downloadable from the Qure portal makes it easier to finalize the radiology report prior to releasing to the patient, especially in a high-volume setting

Dr. Pai reviews and finalizes the qXR report prior to signing it off

Dr. Pai reviews and finalizes the qXR report prior to signing it off

“At SevenHills hospital, we have a daily load of ~220 Chest X-rays from the admitted COVID-19 cases, sometimes going up to 300 films per day. Having qXR has helped me immensely in reading them in a much shorter amount of time and helps me utilise my time more efficiently. The findings from the software are useful to quickly pickup the indications and we have been able to work with the team, and make suitable modifications in the reporting pattern, to make the findings more accurate. qXR pre-fills the report which I review and edit, and this facilitates releasing the patient reports in a much faster and efficient manner. This obviously translates into better patient care and treatment outcomes. The percentage of lung involvement which qXR analyses enhances the Radiologist’s report and is an excellent tool in reporting Chest radiographs of patients diagnosed with COVID infection.”

– Dr Bhujang Pai, Radiology Head, SevenHills Hospital

Challenges and learnings

During the course of the pandemic, Qure has assisted MCGM with providing AI analyses for thousands of chest x-rays of COVID-19 suspects and patients. This has been possible with continued collaboration with key stakeholders within MCGM who have been happy to assist in the process and provide necessary approvals and documentation to initiate work. However, different challenges were posed by the sites owing to their varied nature and the limitations that came with them.

We had to navigate through various technical challenges like interrupted network connections and lack of an IT team, especially at the makeshift COVID centers. We crossed these hurdles repeatedly to ensure that the x-rays from these centers were processed seamlessly within the stipulated timeframe, and the x-ray systems being used were serviced and functioning uninterrupted. Close coordination with the on-ground team and cooperation from their end was crucial to keep the engagement smooth.

This pandemic has been a revelation in many ways. In addition to reiterating that a virus sees no class or creed, it also forced us to move beyond our comfort zones and take our blinders off. Owing to limitations posed by the pandemic and subsequent movement restrictions, every single deployment of qXR by Qure was done entirely remotely. This included end-to-end activities like coordination with the key stakeholders, planning and execution of the deployment of the software, training of on-ground staff, and physicians using the portal/mobile app in addition to continuous operations support.

Robust and smart technology truly made it possible to implement what we had conceived and hoped for. Proving yet again that if we are to move ahead, it has to be a healthy partnership between technology and humanity.

Qure is supported by ACT Grants and India Health Fund for joining MCGM’s efforts for the pandemic response using qXR for COVID-19 management.


An AI Upgrade during COVID-19: Stories from the most resilient healthcare systems in Rural India

When the pandemic hit the world without discretion, it caused health systems to crumble across the world. While a large focus was on strengthening them in the urban cities, the rural areas were struggling to cope up. In this blog, we highlight our experience working with some of the best healthcare centers in rural India that are delivering healthcare to the last mile. We describe how they embraced AI technology during this pandemic, and how it made a difference in their workflow and patient outcomes.

2020 will be remembered as the year of the COVID-19 pandemic. Affecting every corner of the world without discretion, it has caused unprecedented chaos and put healthcare systems under enormous stress. The majority of COVID-19 transmissions take place due to asymptomatic or mildly symptomatic cases. While global public health programs have steadily created evolving strategies for integrative technologies for improved case detection, there is a critical need for consistent and rigorous testing. It is at this juncture that the impact of Qure’s AI-powered chest X-ray screening tool, qXR, was felt across large testing sites such as hospital networks and government-led initiatives.

In India, Qure joined forces with the Indian Government to combat COVID-19 and qXR found its value towards diagnostic aid and critical care management. With the assistance of investor groups like ACT Grants and India Health Fund, we extended support to a number of sites, strengthening the urban systems fighting the virus in hotspots and containment zones.
Unfortunately, by this time, the virus had already moved to the rural areas, crumbling the primary healthcare systems that were already overburdened and resource-constrained.

Discovering the undiscovered healthcare providers

Technologies are meant to improve the quality of human lives, and access to quality healthcare is one of the most basic necessities. To further our work with hospitals and testing centers across the world, we took upon ourselves if more hospitals could benefit from the software in optimising testing capability. Through our physicians, we reached out to healthcare provider networks and social impact organisations that could potentially use the software for triaging and optimisation. During this process, we discovered an entirely new segment, very different from the well equipped urban hospitals we have been operating so far, and interacted with few Physicians dedicated to delivering quality and affordable healthcare through these hospitals.

Working closely with the community public health systems, these secondary care hospitals act as a vital referral link for tertiary hospitals. Some of these are located in isolated tribal areas and address the needs of large catchment populations, hosting close to 100,000 OPD visits annually. They already faced the significant burden of TB and now had to cope with the COVID-19 crisis. With testing facilities often located far away, the diagnosis time increases by days, which is unfortunate because chest X-rays are crucial for primary investigation prior to confirmatory tests, mainly due to the limitations in a testing capacity. No, sufficient testing kits have not reached many parts of rural India as yet!

“I have just finished referring a 25-year-old who came in respiratory distress, flagged positive on X-ray with positive rapid antigen test to Silchar Medical College and Hospital (SMCH), which is 162kms away from here. The number of cases here in Assam is increasing”

Dr. Roshine Koshy, Makunda Christian Leprosy and General Hospital in Assam.

BSTI algorithm

On the left: Chinchpada mission hospital, Maharashtra; Right: Shanti Bhavan Medical Center, Jharkhand.

When we first reached out to these hospitals, we were struck by the heroic vigour with which they were already handling the COVID-19 crisis despite their limited resources. We spoke to the doctors, care-givers and IT experts across all of these hospitals and they had the utmost clarity from the very beginning on how the technology could help them.

Why do they need innovations?

Patients regularly present with no symptoms or atypical ones and conceal their travel history due to the associated stigma of COVID-19. Owing to the ambiguous nature of the COVID-19 presentation, there is a possibility of missing subtle findings. This means that, apart from direct contact with the patient, it puts the healthcare team, their families, and other vulnerable patients at risk.

qXR bridges underlying gaps in these remote, isolated and resource-constrained regions around the world. Perhaps the most revolutionary, life-saving aspect is the fact that, in less than 1 minute, qXR generates the AI analysis of whether the X-ray is normal or abnormal, along with a list of 27+ abnormalities including COVID-19 and TB. With qXR’s assistance, the X-rays that are suggestive of a high risk of COVID-19 are flagged, enabling quick triaging and isolation of these suspects till negative RT PCR confirmatory results are received. As the prognosis changes with co-morbidities, alerting the referring Physician via phone of life-threatening findings like Pneumothorax is an added advantage.

Overview of results generated by qXR

Overview of results generated by qXR

Due to the lack of radiologists and other specialists in their own or neighbouring cities, Clinicians often play multiple roles – Physician, Obstetrician, Surgeon, Intensivist, Anaesthesist – and is normal in these hospitals that investigate, treat and perform surgeries for those in need. Detecting any case at risk prior to their surgical procedures are important for necessitating RT PCR confirmation and further action.

Enabling the solution and the impact

These hospitals have been in the service of the local communities with a mix of healthcare and community outreach services for decades now. Heavily dependent on funding, these setups have to often navigate severe financial crises in their mission to continue catering to people at the bottom of the pyramid. Amidst the tribal belt in Jharkhand, Dr. George Mathew (former Principal, CMC, Vellore) and Medical Director of Shantibhavan Medical Center in Simdega, had to face the herculean task of providing food and accommodation for all his healthcare and non-healthcare staff as they were ostracised by their families owing to the stigma attached to COVID-19 care. Lack of availability of  PPE kits and other protective gear, also pushed these sites to innovate and produce them inhouse.

Staff protecting themselves and patients

Left: the staff of Shanti Bhavan medical center making the essentials for protecting themselves in-house; Right: staff protecting themselves and a patient.

qXR was introduced to these passionate professionals and other staff were sensitized on the technology. Post their buy-in of the solution, we on-boarded 11 of these hospitals, working closely with their IT teams for secure protocols, deployment and training of the staff in a span of 2 weeks. A glimpse of the hospitals as below:

LocationHospital NameSetting
Betul District, rural Madhya PradeshPadhar HospitalThis is a 200 bedded multi-speciality charitable hospital engages in a host of community outreach activities in nearby villages involving education, nutrition, maternal and child health programs, mental health and cancer screening
Nandurbar, MaharashtraChinchpada Mission HospitalThis secondary care hospital serves the Bhil tribal community. Patients travel upto 200kms from the interiors of Maharashtra to avail affordable, high quality care.
Tezpur, AssamThe Baptist Christian HospitalThis is a 200- bedded secondary care hospital in the North eastern state of Assam
Bazaricherra, AssamMakunda Christian Leprosy & General HospitalThey cater to the tribal regions. Situated in a district with a Maternal Mortality Rate (MMR) as high as 284 per 100,000 live births and Infant Mortality Rate (IMR) of 69 per 1000 live births. They conduct 6,000 deliveries, and perform 3,000 surgeries annually.
Simdega, JharkhandShanti Bhavan Medical CenterThis secondary hospital caters to remote tribal district. It is managed entirely by 3-4 doctors that actively multitask to ensure highest quality care for their patients. The nearest tertiary care hospital is approximately 100 km away. Currently, they are a COVID-19 designated center and they actively see many TB cases as well.

Others include hospitals in Khariar, Odisha; Dimapur, Nagaland; Raxaul, Bihar and so on.

Initially, qXR was used to process X-rays of cases with COVID-19 like symptoms, with results interpreted and updated in a minute. Soon the doctors found it to be useful in OPD as well and the solution’s capability was extended to all patients who visited with various ailments that required chest X-ray diagnosis. Alerts on every suspect are provided immediately, based on the likelihood of disease predicted by qXR, along with information on other suggestive findings. The reports are compiled and integrated on our patient workflow management solution, qTrack. Due to resource constraints for viewing X-ray in dedicated workstations, the results are also made available real-time using the qTrack mobile application.

qTrack app and web

Left: qTrack app used by the Physicians to view results in real time during while they are attending patients and performing routine work; Right: qTrack web used by Physicians and technicians to view instantaneously for reporting.

“It is a handy tool for our junior medical officers in the emergency department, as it helps in quick clinical decision making. The uniqueness of the system being speed, accuracy, and the details of the report. We get the report moment the x rays are uploaded on the server. The dashboard is very friendly to use. It is a perfect tool for screening asymptomatic patients for RT PCR testing, as it calculates the COVID-19 risk score. This also helps us to isolate suspected patients early and thereby helping in infection control. In this pandemic, this AI system would be a valuable tool in the battleground”

Dr Jemin Webster, Tezpur Baptist Hospital

Once the preliminary chest X-ray screening is done, the hospitals equipped with COVID-19 rapid tests get them done right away, while the others send samples to the closest testing facility which may be more than 30 miles away, with results made available in 4-5 days or more. But, none of these hospitals have the RT-PCR testing facility, yet!

qXR Protocol

In Makunda Hospital, Assam, qXR is used as an additional input in the diagnosis methodologies to manage the patient as a COVID-19 patient. They have currently streamlined their workflow to include the X-ray technicians taking digital X-rays and uploading the images on qXR, to  follow up and alert the doctors. Meanwhile, physicians can also access reports, review images  and make clinical corroboration anywhere they are through qTrack and manage patients without any undue delay.

Dr. Roshine Koshy using qXR

Dr. Roshine Koshy using qXR system during her OPD to review and take next course of action

“One of our objectives as a clinical team has been to ensure that care for non-COVID-19 patients is not affected as much as possible as there are no other healthcare facilities providing similar care. We are seeing atypical presentations of the illness, patients without fever, with vague complaints. We had one patient admitted in the main hospital who was flagged positive on the qXR system and subsequently tested positive and referred to a higher center. All the symptomatic patients who tested positive on the rapid antigen test have been flagged positive by qXR and some of them were alerted because of the qXR input. Being a high volume center and the main service provider in the district, using as a triaging tool will have enormous benefits in rural areas especially where there are no well-trained doctors”

– Dr. Roshine Koshy, Makunda Christian Leprosy and General Hospital in Assam.

There are a number of changes our users experienced in this short span of introduction of qXR in their existing workflow including:

  • Empowering the front-line healthcare physicians and care-givers in quick decisions
  • Enabling diagnosis for patients by triaging them for Rapid Antigen or RT-PCR tests immediately
  • Identifying asymptomatic cases which would have been missed otherwise
  • Ensuring safety of the health workers and other staff
  • Reducing risk of disease transmission

In Padhar Hospital, Madhya Pradesh, in addition to triaging suspected COVID cases, qXR assists doctors in managing pre-operative patients, where their medicine department takes care of pre-anaesthesia checkups as well. qXR helps them in identifying and flagging off suspected cases who are planned for procedures.  They are deferred till diagnosis or handled with appropriate additional safety measures in case of an emergency.

“We are finding it quite useful since we get a variety of patients, both outpatients and inpatients. And anyone who has a short history of illness and has history suggestive of SARI, we quickly do the chest X-ray and if the Qure app shows a high COVID-19 score, we immediately refer the patient to the nearby district hospital for RT-PCR for further management. Through the app we are also able to pick up asymptomatic suspects who hides their travel history or positive cases who have come for second opinion, to confirm and/or guide them to the proper place for further testing and isolation”

– Dr Mahima Sonwani, Padhar Hospital, Betul, Madhya Pradesh

Dr. Roshine Koshy using qXR

Left: technician capturing X-ray in Shanti Bhavan medical center; Right: Dr. Jemine Webster using qXR solution in Baptist hospital, Tezpur

In some of the high TB burden settings like Simdega in Jharkhand, qXR is used as a surveillance tool for screening and triaging Tuberculosis cases in addition to COVID-19 and other lung ailments.

“We are dependent on chest X-rays to make the preliminary diagnosis in both these conditions before we perform any confirmatory test. There are no trained radiologists available in our district or our neighbouring district and struggle frequently to make accurate diagnosis without help of a trained radiologist. The AI solution provided by Qure, is a perfect answer for our problem in this remote and isolated region. I strongly feel that the adoption of AI for Chest X-ray and other radiological investigation is the ideal solution for isolated and human resource deprived regions of the world”

– Dr.George Mathew, Medical Director, Shanti Bhavan Medical Centre

Currently, qXR processes close to 150 chest X-rays a day from these hospitals, enabling quick diagnostic decisions for lung diseases.

Challenges: Several hospitals had very basic technological infrastructure systems with poor internet connectivity and limitations in IT systems for using all supporting softwares. They were anxious about potential viruses / crashing the computer where our software was installed. Most of these teams had limited understanding of exposure to working with such softwares as well. However, they were extremely keen to learn, adapt and even provide solutions to overcome these infrastructural limitations. The engineers of the customer success team at Qure, deployed the software gateways carefully, ensuring no interruption in their existing functioning.


At Qure, we have worked closely with public health stakeholders in recent years. It is rewarding to hear the experiences and stories of impact from these physicians. To strengthen their armor in the fight against the pandemic even in such resource-limited settings, we will continue to expand our software solutions. Without limitation, qXR will be available across primary, secondary, and tertiary hospitals. The meetings, deployments, and training will be done remotely, providing a seamless experience. It is reassuring to hear these words:

“Qure’s solution is particularly attractive because it is cutting edge technology that directly impacts care for those sections of our society who are deprived of many advances in science and technology simply because they never reach them! We hope that this and many more such innovative initiatives would be encouraged so that we can include the forgotten masses of our dear people in rural India in the progress enjoyed by those in the cities, where most of the health infrastructure and manpower is concentrated”

Dr. Ashita Waghmare, Chinchpada hospital

Democratizing healthcare through innovations! We will be publishing a detailed study soon.


Re-purposing qXR for COVID-19

In March 2020, we re-purposed our chest X-ray AI tool, qXR, to detect signs of COVID-19. We validated it on a test set of 11479 CXRs with 515 PCR-confirmed COVID-19 positives. The algorithm performs at an AUC of 0.9 (95% CI : 0.88 - 0.92) on this test set. At our most common operating threshold for this version, sensitivity is 0.912 (95% CI : 0.88 - 0.93) and specificity is 0.775 (95% CI : 0.77 - 0.78). qXR for COVID-19 is used at over 28 sites across the world to triage suspected patients with COVID-19 and to monitor the progress of infection in patients admitted to hospital

The emergence of the COVID-19 pandemic has already caused a great deal of disruption around the world. Healthcare systems are overwhelmed as we speak, in the face of WHO guidance to ‘test, test, test’ [1]. Many countries are facing a severe shortage of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests. There has been a lot of debate around the role of radiology — both chest X-rays (CXRs) and chest CT scans — as an alternative or supplement to RT-PCR in triage and diagnosis. Opinions on the subject range from ‘Radiology is fundamental in this process’ [2] to ‘framing CT as pivotal for COVID-19 diagnosis is a distraction during a pandemic, and possibly dangerous’ [3].

Role of Radiography

The humble chest X-ray has emerged as the frontline screening and diagnostic tool for COVID-19 infection in a few countries and is used in conjunction with clinical history and key blood markers such as C-Reactive Protein (CRP) test and lymphopenia [4]. Ground glass opacities and consolidations which are peripheral and bilateral in nature are attributed to be the most common findings with respect to COVID related infections on CXRs and chest CTs. CXRs can help in identifying COVID-19 related infections and can be used as a triage tool in most cases. In fact, Italian and British hospitals are employing CXR as a first-line triage tool due to high RT-PCR turnaround times. A recent study [5] which examined CXRs of 64 patients found that in 9% of cases, initial RT-PCR was negative whereas CXRs showed abnormalities. All these cases subsequently tested positive for RT-PCR within 48 hours. The American college of Radiology recommends considering portable chest X-rays [6] to avoid bringing patients to radiography rooms. The Canadian Association of Radiologists suggest the use of mobile chest X-ray units for preliminary diagnosis of suspected cases [7] and to monitor critically ill patients, but have reported that no abnormalities are seen on CXRs in the initial stages of the infection.

BSTI algorithm

Radiology decision tool for suspected COVID-19 – The British Society of Thoracic Imaging [8]

As of today, despite calls for opening up imaging data on COVID-19 and outstanding efforts from physicians on the front-lines, there are limited X-ray or CT datasets in the public domain pertaining specifically to COVID. These datasets remain insufficient to train an AI model for COVID-19 triage or diagnosis but are potentially useful in evaluating the model – provided the model hasn’t been trained on the same data sources.

Building and evaluating qXR for COVID-19

Over the last month, customers, collaborators, healthcare providers, NGOs, state and national governments have reached out to us for help with COVID detection on chest X-rays and CTs.

In response, we have adapted our tried-and-tested chest X-ray AI tool, qXR to identify findings related to COVID-19 infections. qXR is trained using a dataset of 2.5 million chest X-rays (that included bacterial and viral pneumonia and many other chest X-ray findings) and is currently deployed in over 28 countries. qXR detects the following findings that are indicative of COVID-19: Opacities and Consolidation with bilateral and peripheral distribution and the following findings that are contra-indicative of COVID-19: hilar enlargement, discrete pulmonary nodule, calcification, cavity and pleural effusion.

These CE-marked capabilities have been leveraged for a COVID-19 triage product that is highly sensitive to COVID-19 related findings. This version of qXR gives out the likelihood of a CXR being positive for COVID-19, called Covid-19 Risk. Covid-19 Risk is computed using a post processing algorithm which combines the model outputs for the above mentioned findings. The algorithm is tuned on a set of 300 COVID-19 positives and 300 COVID-19 negatives collected from India and Europe.

Most new qXR users for COVID-19 are using it as a triage tool, often in settings with limited diagnostic resources. This version of qXR also localizes and quantifies the affected region. This capability is being used to monitor the progression of infection and to evaluate response to treatment in new clinical studies.

qXR sample

Sample Output of qXR [9]

Evaluation of the algorithm

We have created an independent testset of 11479 CXRs to evaluate our algorithm. The WHO [10] recommends a confirmatory diagnosis of COVID-19 using Reverse-Transcriptase Polymerase Chain Reaction (RT-PCR) – a specialised Nucleic Acid Amplification Test (NAAT) which looks for unique signatures using primers designed for the COVID-19 RNA sequence. Positives in this test set are defined as any CXR that is acquired while the patient has tested positive on RT-PCR test based on sputum/ lower respiratory and or upper respiratory aspirates/throat swab samples for COVID-19. Negatives in this test set are defined as any CXR which was acquired before the first case of COVID-19 was discovered.

The size of the negative set relative to the positive set was set to match the available prevalence in the literature [11]. The test set has 515 positives and 10964 negatives. Negatives are sampled from an independent set 250,000 CXRs. Negative set has 1609 cases of bilateral opacity and 547 cases of pulmonary consolidation in it (findings which are indicative of COVID-19 on a CXR), where the final diagnosis is not COVID-19. Negative set also has 355 non-opacity related abnormalities. This allowed us to evaluate algorithms ability to detect non COVID-19 opacities and findings, and is used to suggest alternative possible etiology and rule out COVID-19. We have used Area under Receiver Operating Characteristic Curve (AUC) along with Sensitivity and Specificity at the operating point to evaluate the performance of our algorithm.


Number of scans11479
Other Abnormalities355

Test set demographics

A subset (1000 cases) of this test set was independently reviewed by radiologists to create pixel level annotations to localize opacity and consolidation. Localization and progression monitoring capability of qXR is validated by computing the Jaccard Index between algorithm output and radiologist annotations.


To detect signs of COVID-19, We have observed an AUC of 0.9 (95% CI: 0.88 - 0.92) on this test set. At the operating threshold, we have observed the sensitivity to be 0.912 (95% CI : 0.88 - 0.93) and specificity to be 0.775 (95% CI : 0.77 - 0.78). While there are no WHO guidelines yet for an imaging based triage tool for COVID, WHO recommends a minimum sensitivity and specificity of 0.9 and 0.7 for community screening tests for Tuberculosis [12], which is a deadly infectious disease in itself. We have observed a Jaccard index of 0.88 between qXR’s output and expert’s annotations.

ROC Curve

Receiver Operating Characteristic Curve

Deploying qXR for COVID-19

qXR is available as a web-api and can be deployed within minutes. Built using our learnings of deploying globally and remotely, it can interface with a variety of PACS and RIS systems, and is very intuitive to interpret. qXR can be used to triage suspect patients in resource constrained countries to make effective use of RT-PCR test kits. qXR is being used for screening and triage at multiple hospitals in India and Mexico.

San Raffaele Hospital in Milan, Italy has deployed qXR to monitor patients and to evaluate patient’s response to treatments. In Karachi, qXR powered mobile vans are being used at multiple sites to identify potential suspects early and thus reducing burden on the healthcare system.

qXR deployments

Timeline of qXR for COVID

In the UK, all the suspected COVID-19 patients presenting to the emergency department are undergoing blood tests and CXR [4]. This puts a tremendous amount of workload on already burdened radiologists as it becomes critical for radiologists to report the CXRs urgently. qXR, with its ability to handle huge workloads, provides significant value in such a scenario and thus reduce the burden on radiologists.

qXR can also be scaled for rapid and extensive population screening. Frontline clinicians are increasingly relying on chest X-rars to triage the sickest patients, while they await RT-PCR results. When there is high clinical suspicion for COVID-19 infection, the need for a patient with positive chest X-ray to get admitted in a hospital is conceivable. qXR can help solve this problem at scale.

qXR deployments

Impact of qXR for COVID-19

Work with us

With new evidence published every day, and evolving guidance and protocols adapting in suit for COVID-19, national responses globally remain fluid. Singapore, Taiwan and South Korea have shown that aggressive and proactive testing plays a crucial role in containing the spread of the disease. We believe qXR can play an important role in expanding screening in the community to help reduce the burden on healthcare systems. If you want to use qXR, please reach out to us.


  1. WHO Director-General’s opening remarks at the media briefing on COVID-19 – WHO, Accessed Apr 9, 2020.
  2. Imaging the coronavirus disease COVID-19 – Healthcare in Europe Website, Accessed Apr 9, 2020.
  3. Hope et al. A role for CT in COVID-19? What data really tell us so far – The Lancet, Mar 27, 2020
  4. Lessons from the frontline of the covid-19 outbreak – BMJ Blog, Accessed Apr 9, 2020.
  5. Wong et al. Frequency and Distribution of Chest Radiographic Findings in COVID-19 Positive Patients – Radiology, Mar 27, 2020.
  6. ACR Recommendations for the use of Chest Radiography and Computed Tomography (CT) for Suspected COVID-19 Infection – ACR, Accessed Apr 9, 2020.
  7. Lei et al. COVID-19 Infection: Early Lessons – Canadian Association of Radiologists Journal, Mar 12, 2020.
  8. Radiology decision tool for suspected COVID-19 – The British Society of Thoracic Imaging, Accessed Apr 9, 2020.
  9. Cohen et al. COVID-19 image data collection – arXiv:2003.11597, 2020
  10. Laboratory testing for 2019 novel coronavirus (2019-nCoV) in suspected human cases – WHO, Accessed Apr 9, 2020.
  11. Verity et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis – The Lancet Infectious Diseases, Mar, 2020.
  12. High priority target product profiles for new tuberculosis diagnostics: report of a consensus meeting, tech. rep., World Health Organization, Apr 28-29, 2014.


Improving performance of AI models in presence of artifacts

Our deep learning models have become really good at recognizing hemorrhages from Head CT scans. Real-world performance is sometimes hampered by several external factors both hardware-related and human-related. In this blog post, we analyze how acquisition artifacts are responsible for performance degradation and introduce two methods that we tried, to solve this problem.

Medical Imaging is often accompanied by acquisition artifacts which can be subject related or hardware related. These artifacts make confident diagnostic evaluation difficult in two ways:

  • by making abnormalities less obvious visually by overlaying on them.
  • by mimicking an abnormality.

Some common examples of artifacts are

  • Clothing artifact- due to clothing on the patient at acquisition time See fig 1 below. Here a button on the patient’s clothing looks like a coin lesion on a Chest X Ray. Marked by red arrow.

clothing artifact

Fig 1. A button mimicking coin lesion in Chest X Ray. Marked by red arrow.Source.

  • Motion artifact- due to voluntary or involuntary subject motion during acquisition. Severe motion artifacts due to voluntary motion would usually call for a rescan. Involuntary motion like respiration or cardiac motion, or minimal subject movement could result in artifacts that go undetected and mimic a pathology. See fig 2. Here subject movement has resulted in motion artifacts that mimic subdural hemorrhage(SDH).

motion artifact

Fig 2. Artifact due to subject motion, mimicking a subdural hemorrhage in a Head CT.Source

  • Hardware artifact- See fig 3. This artifact is caused due to air bubbles in the cooling system. There are subtle irregular dark bands in scan, that can be misidentifed as cerebral edema.

hardware artifact edema

Fig 3. A hardware related artifact, mimicking cerebral edema marked by yellow arrows.Source

Here we are investigating motion artifacts that look like SDH, in Head CT scans. These artifacts result in increase in false positive (FPs) predictions of subdural hemorrhage models. We confirmed this by quantitatively analyzing the FPs of our AI model deployed at an urban outpatient center. The FP rates were higher for this data when compared to our internal test dataset.
The reason for these false positive predictions is due to the lack of variety of artifact-ridden data in the training set used. Its practically difficult to acquire and include scans containing all varieties of artifacts in the training set.

artifact mistaken for sdh

Fig 4. Model identifies an artifact slice as SDH because of similarity in shape and location. Both are hyperdense areas close to the cranial bones

We tried to solve this problem in the following two ways.

  • Making the models invariant to artifacts, by explicitly including artifact images into our training dataset.
  • Discounting slices with artifact when calculating the probability of bleed in a scan.

Method 1. Artifact as an augmentation using Cycle GANs

We reasoned that the artifacts were misclassified as bleeds because the model has not seen enough artifact scans while training.
The number of images containing artifacts is relatively small in our annotated training dataset. But we have access to several unannotated scans containing artifacts acquired from various centers with older CT scanners.(Motion artifacts are more prevalent when using older CT scanners with poor in plane temporal resolution). If we could generate artifact ridden versions of all the annotated images in our training dataset, we would be able to effectively augment our training dataset and make the model invariant to artifacts.
We decided to use a Cycle GAN to generate new training data containing artifacts.

Cycle GAN[1] is a generative adversarial network that is used for unpaired image to image translation. It serves our purpose because we have an unpaired image translation problem where X domain has our training set CT images with no artifact and Y domain has artifact-ridden CT images.

cycle gan illustration

Fig 5. Cycle GAN was used to convert a short clip of horse into that of a zebra.Source

We curated a A dataset of 5000 images without artifact and B dataset of 4000 images with artifacts and used this to train the Cycle GAN.

Unfortunately, the quality of generated images was not very good. See fig 6.
The generator was unable to capture all the variety in CT dataset, meanwhile introducing artifacts of its own, thus rendering it useless for augmenting the dataset. Cycle GAN authors state that the performance of the generator when the transformation involves geometric changes for ex. dog to cat, apples to oranges etc. is worse when compared to transformation involving color or style changes. Inclusion of artifacts is a bit more complex than color or style changes because it has to introduce distortions to existing geometry. This could be one of the reasons why the generated images have extra artifacts.

cycle gan images

Fig 6. Sampling of generated images using Cycle GAN. real_A are input images and fake_B are the artifact_images generated by Cycle GAN.

Method 2. Discounting artifact slices

In this method, we trained a model to identify slices with artifacts and show that discounting these slices made the AI model identifying subdural hemorrhage (SDH) robust to artifacts.
A manually annotated dataset was used to train a convolutional neural network (CNN) model to detect if a CT slice had artifacts or not. The original SDH model was also a CNN which predicted if a slice contained SDH. The probabilities from artifact model were used to discount the slices containing artifact and artifact-free slices of a scan were used in computation of score for presence of bleed.
See fig 7.

Method 2 illustration

Fig 7. Method 2 Using a trained artifacts model to discount artifact slices while calculating SDH probability.


Our validation dataset contained 712 head CT scans, of which 42 contained SDH. Original SDH model predicted 35 false positives and no false negatives. Quantitative analysis of FPs confirmed that 17 (48%) of them were due to CT artifacts. Our trained artifact model had slice-wise AUC of 96%. Proposed modification to the SDH model had reduced the FPs to 18 (decrease of 48%) without introducing any false negatives. Thus using method 2, all scanwise FP’s due to artifacts were corrected.

In summary, using method 2, we improved the precision of SDH detection from 54.5% to 70% while maintaining a sensitivity of 100 percent.

confusion matrics

Fig 8. Confusion Matrix before and after using artifact model for SDH prediction

See fig 9. for model predictions on a representative scan.

artifact discount explanaation

Fig 9. Model predictions for few representative slices in a scan falsely predicted as positive by original SDH model

A drawback of Method 2 is that if SDH and artifact are present in the same slice, its probable that the SDH could be missed.


Using a cycle GAN to augment the dataset with artifact ridden scans would solve the problem by enriching the dataset with both SDH positive and SDH negative scans with artifacts over top of it. But the current experiments do not give realistic looking image synthesis results. The alternative we used, meanwhile reduces the problem of high false positives due to artifacts while maintaining the same sensitivity.


  1. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks by Jun-Yan Zhu et al.


Scaling up TB screening with AI: Deploying automated X-ray screening in remote regions

We have been deploying our deep learning based solutions across the globe. qXR, our product for automated chest X-ray reads, is being widely used for Tuberculosis screening. In this blog, we will understand the scale of the threat that TB presents. Thereafter, taking one of our deployments as a case study, we will explain how artificial intelligence can help us in fighting TB.’s deep learning solutions are actively reading radiology images in over 82 sites spread across 12 countries. We have processed more than 50 thousand scans till date. One of the major use cases of our solutions is for fast-tracking Tuberculosis (TB) screening.

Understanding TB

TB is caused by bacteria called Mycobacterium tuberculosis and it mostly affects the lungs. About one-fourth of the world’s population is infected by the bacteria inactively – a condition called latent TB. TB infection occurs when a person breathes in droplets produced due to an active TB person’s coughing, sneezing or spitting.

TB is a curable and preventable disease. Despite that, WHO reports that it is one of the top 10 causes of deaths worldwide. In 2017, 10 million people fell ill with TB, out of which 1.6 million lost their lives. 1 million children got affected by it, with 230,000 fatalities. It is also the leading cause of death among HIV patients.

Diagnosis of TB

There are many tests to detect TB. Some of them are as follows:

  • Chest X-ray: Typically used to screen for signs of TB in the lungs. They are a sensitive and inexpensive screening test, but may pick up other lung diseases too. So chest X-rays are not used for a final TB diagnosis. The presence of TB bacteria is confirmed using a bacteriological or molecular test of sputum or other biological sample.
  • Sputum tests: The older AFB sputum tests (samples manually viewed through a microscope looking for signs of bacteria) are still used in low-income countries to confirm TB. A more sensitive sputum test that uses DNA amplification technology to detect traces is now in wide use to confirm TB – it’s not only more sensitive, but also can also look for TB resistance. Tests like Genexpert and TrueNat fall under this category. These are fairly expensive tests.

Molecular tests have shown excellent results in South Africa and are generally considered as the go-to test for TB. However, their high costs make it impossible to conduct them for every TB suspect.

Failure in early detection

Due to the high costs of molecular tests, Chest X-rays are generally preferred as a pre-test for TB suspects. Post that, sputum or molecular tests are performed for confirmation. In regions where these confirmatory tests are not available, Chest X-rays are used for final diagnosis.

Having understood the X-rays’ key role in TB diagnosis, it is important to note that there is a huge dearth of radiologists to read these X-rays. In India alone, 80 million chest X-rays are being captured every year. There aren’t enough radiologists to read them within acceptable timelines. Depending upon the extent of shortage for radiology expertise, it can take anywhere between 2 to 15 days for the report to arrive. As a result, critical time is lost for a TB patient which prevents its early detection. A failure in detecting it early is not only hazardous for the patient but also enhances the risk of its transmission to others.

Moreover, the error rates in reading these X-rays lie around 25-30%. Such errors can prove to be fatal for the patient.

TB diagnosis

Where comes into the picture

This large gap between the number of TB incidences and the number of timely & accurately reported cases is a major reason why many lives are lost to this curable disease. It can be bridged with a solution that requires little manual intervention. This is precisely how Qure’s qXR solution, trained on more than a million chest X-rays, attacks at the heart of the problem. The AI (Artificial Intelligence) encapsulated inside qXR automates reading chest X-rays and generates reports within seconds. Thereby, reducing the waiting time for TB confirmatory tests from weeks to a couple of hours and enrolling confirmed cases to treatment the same day!

qXR features

qXR features

While bacteriological confirmatory tests on presumptive cases are preferred in a screening setting, the cost burden increases. Sputum culture testing will take weeks for the reports that could result in dropouts in collecting reports and treatment enrolment. Additionally, the shortage of sourcing Cartridge Based Nucleic Acid Amplification Test (CB-NAAT) becomes a limitation which results in a delay of the testing process.’s qXR helps in cutting down on time and costs incurred by reducing the number of individuals required to go through these tests. The whole program workflow happens as depicted in the following picture.

Patient flow

Case Study: AccessTB, Philippines

While upscaling our solutions in the last 2 years, it has become evident that can play a vital role in humanity’s war against TB. We deployed qXR with ACCESS TB Project in Philippines in their TB screening program. During the deployment, we learned the operational dynamics of deploying Artificial Intelligence (AI) at health centers.

TB screening process before incorporating qXR

The ACCESS TB program has mobile vans equipped with X-rays machines with trained radiographers and health workers. The program is intended to screen presumptive cases and individuals with a high-risk factor of TB, by running the vans across different cities in the Philippines. Screening camps are either announced in conjunction with a nearby nursing home or health workers identify and invite individuals at risk on the days of programs.

The vans leave the office on Monday morning for remote villages with a predefined schedule. These villages are situated around 100kms from Manila. Two radiology technicians accompany each van. Once they reach the desired health center in the village, they start capturing X-rays for each individual. The X-ray machines are connected to a computer which stores these X-rays locally. One can also edit the dicom (radiology image) information inside the X-ray from this computer.

Individuals arrive inside the van on a first come first serve basis. They are given a receipt containing their patient id, name, etc. Their X-ray is also marked with the same ID using the computer. This approach of mass screening for TB is similar to the approach adopted by the USA during the 1930s to 1960s as depicted in the following picture.

TB screening van

Mass radiography screening campaigns in USA during 1930s to 1960s (Source)

Once all the X-rays have been captured, the vans return to their stay in the same village. They visit a new village/ health center on subsequent weekdays. On Friday evening, all the vans return to Manila. Thereafter, all the X-rays captured in the 4 vans over the week are sent to a radiologist for review. The lead time for the radiologist report is 3 working days and can extend to 2 weeks. The delay in reporting leads to delay in diagnosis and treatment, which can prove to be fatal for the patient and the neighborhood.

Access TB van

Front & side view of AccessTB van with individuals queuing inside the van

Challenges for

Our team arrived in Manila during the second week of September 2018 with the deep learning solution sitting nice and cozy on the cloud. The major challenges in front of us were two-fold:

  1. To ensure smooth upload of images to our cloud server: This was a challenge because some of the villages and towns being visited were really remote and there was no guarantee of sufficient internet connection for the upload to work properly. We had to make sure that everything worked fine even if there was no internet connectivity. To deal with this, we built an application which was installed on their computer to upload images on our cloud. In case of no internet connectivity, it would store all the information and wait for better connectivity. As soon as connectivity became available, the app would start processing deferred uploads.
  2. To enable end to end patient management on one single platform: This was the biggest concern and we designed the software to minimize manual intervention at various stages.

We built a portal where radiology assistants could register patients, radiologists could report on them and patient history could be maintained. The diagnosis from the radiologist, qXR and CB-NAAT tests are all accumulated at a single place.

QXR Portal

Snapshot of complete patient management system

Features that could ease the workflow were added to the software that enabled the staff in the field to filter patients by name, date, site or health center. Such features and provisions in the software helped the staff to capture the progress of screening for a patient with simple sorting and searches.

Implementation process

At Qure, we deliver our products and solutions understanding the customer needs and designing workflows to fit into their existing processes. Especially when it comes to mass screening programs, we understand that each one of them is uniquely designed by program managers & strategists, and requires specific customizations to deliver a seamless experience.

After understanding the existing workflow at AccessTB, we designed our software to include elements that can automate some of the existing processes. Thereafter, the software was built, tested, packaged and stored in a secure cloud.
We figured the best way to integrate with their existing X-ray console and completed the integration on all the vans in 2 working days’ time.

A field visit was arranged after the deployment to assess the software’s performance in areas with limited network connectivity and its ease of usage for the radiology staff. Based on our on-field learnings, we further customized the software’s workflow for the staff.

The implementation process ended with a classroom training program with the field staff, technicians and program managers. With the completion of the deployment, software adaptability assessment and training, we handed over the software to the program in 5 days before we left Manila.

Radiology Assistant Training

Training program for radiology assistants post qXR deployment

Quoting Preetham Srinivas (AI scientist at Qure) on qXR, “With qXR at the heart of it, we’ve developed a solution that is end to end. As in, with individual registrations, and then qXR doing the automated analysis and flagging individuals who need microbiological confirmation. Radiologists can verify in the same portal and then, an organization doing the microbiological tests can plug in their data in the same portal. And once you have a dashboard which collates all this information in one place, it becomes very powerful. The loss itself can be minimized. It becomes that much easier to track the person and make sure he is receiving the treatment.”


WHO has given the status of an epidemic to TB. They adopted an END TB strategy in 2014 aimed at reducing TB deaths by 90% and cutting new cases by 80% between 2015 and 2030. Ending TB by 2030 is one of the health targets of their Sustainable Development Goals.

The scale of this epidemic cries out for technology to intervene. Technologies like AI, if incorporated into existing TB care ecosystem, can not only assist healthcare practitioners massively, but also enrich it by the supplied data and feedback. And this is not a mere speculation. With qXR, we are having a first-hand experience of how AI can accelerate our efforts in eradicating TB. Jerome Trinona, account coordinator for AccessTB project, says “’s complete TB software is very helpful in maximizing our time – now we can keep track of the entire patient workflow in one place.”

Access TB success

Successful deployment of qXR with AccessTB Program staff

Successful deployments like AccessTB show that is leading the battle against TB at the technology and innovation fronts. Post World TB day, let us all embrace AI as our newest ammunition against TB.

Let’s join hands to end TB by 2030. 1

  1. Reach out to us at 


Morphology of the Brain: Changes in Ventricular and Cranial Vault Volumes in 15000 subjects with Aging and Hydrocephalus

This post is Part 1 of a series that uses large datasets (15,000+) coupled with deep learning segmentation methods to review and maybe re-establish what we know about normal brain anatomy and pathology. Subsequent posts will tackle intra-cranial bleeds, their typical volumes and locations across similarly sized datasets.

Brain ventricular volume has been quantified by post-mortem studies [1] and pneumoencephalography. When CT and subsequently MRI became available, facilitating non-invasive observation of the ventricular system larger datasets could be used to study these volumes. Typical subject numbers in recent studies have ranged from 50 – 150 [26].

Now that deep learning segmentation methods have enabled automated precise measurements of ventricular volume, we can re-establish these reference ranges using datasets that are 2 orders of magnitude larger. This is likely to be especially useful for age group extremes – in children, where very limited reference data exist and the elderly, where the effects of age-related atrophy may co-exist with pathologic neurodegenerative processes.

To date, no standard has been established regarding the normal ventricular volume of the human brain. The Evans index and the bicaudate index are linear measurements currently being used as surrogates to provide some indication that there is abnormal ventricular enlargement [1]. True volumetric measures are preferable to these indices for a number of reasons [7, 8] but have not been adopted so far, largely because of the time required for manual segmentation of images. Now that automated precise quantification is feasible with deep learning, it is possible to upgrade to a more precise volumetric measure.

Such quantitative measures will be useful in the monitoring of patients with hydrocephalus, and as an aid to diagnosing normal pressure hydrocephalus. In the future, automated measurements of ventricular, brain and cranial volumes could be displayed alongside established age- and gender-adjusted normal ranges as a standard part of radiology head CT and MRI reports.

Methods and Results

To train our deep learning model, lateral ventricles were manually annotated in 103 scans. We split these scans randomly with a ratio of 4:1 for training and validation respectively. We trained a U-Net to segment lateral ventricles in each slice. Another U-Net model was trained to segment cranial vault using a similar process. Models were validated using DICE score metric versus the annotations.

AnatomyDICE Score

Lateral Ventricles0.909
Cranial Vault0.983

Validation set of about 20 scans might not have represented all the anatomical/pathological variations in the population. Therefore, we visually verified that the resulting models worked despite pathologies like hemorrhage/infarcts or surgical implants such as shunts. We show some representative scans and model outputs below.

Focal ventricle dilatation

30 year old male reported with 'focal dilatation of left lateral ventricle.'

Mild Hydrcephalus

7 year old female child reported with 'mild obstructive hydrocephalus'

Mild Hydrcephalus

28 year old male reported with fracture and hemorrhages


36 year old male reported with an intraventricular mass and with a VP shunt

To study lateral ventricular and cranial vault volume variation across the population, we randomly selected 14,153 scans from our database. This selection contained only 208 scans with hydrocephalus reported by the radiologist. Since we wanted to study ventricle volume variation in patients with hydrocephalus, we added 1314 additional scans reported with ‘hydrocephalus’. We excluded those scans for which age/gender metadata were not available.
In total, our analysis dataset contained 15223 scans whose demographic characteristics are shown in the table below.


Number of scans15223
Females6317 (41.5%)
Age: median (interquartile range)40 (24 – 56) years
Scans reported with cerebral atrophy1999 (13.1%)
Scans reported with hydrocephalus1404 (9.2%)

Dataset demographics and prevalances.

Histogram of age distribution is shown below. It can be observed that there are reasonable numbers of subjects (>200) for all age and sex groups. This ensures that our analysis is generalizable.

age histogram

We ran the trained deep learning models and measured lateral ventricular and cranial vault volumes for each of the 15223 scans in our database. Below is the scatter plot of all the analyzed scans.

Scatter plot

In this scatter plot, x-axis is the lateral ventricular volume while y-axis is cranial vault volume. Patients with atrophy were circled with marked orange and while scans with hydrocephalus were marked with green. Patients with atrophy were on the right to the majority of the individuals, indicating larger ventricles in these subjects. Patients with hydrocephalus move to the extreme right with ventricular volumes even higher than those with atrophy.

To make this relationship clearer, we have plotted distribution of ventricular volume for patients without hydrocephalus or atrophy and patients with one of these.

ventricular volume distribution

Interestingly, hydrocephalus distribution has a very long tail while distribution of patients with neither hydrocephalus nor atrophy has a narrower peak.

Next, let us observe cranial vault volume variation with age and sex. Bands around solid lines indicate interquartile range of cranial vault volume of the particular group.

cranial vault volume variation

An obvious feature of this plot is that the cranial vault increases in size until age of 10-20 after which it plateaus. The cranial vault of males is approximately 13% larger than that of females. Another interesting point is that the cranial vault in males will grow until the age group of 15-20 while in the female group it stabilizes at ages of 10-15.

Now, let’s plot variation of lateral ventricles with age and sex. As before, bands indicate interquartile range for a particular age group.

lateral ventricular volume variation

This plot shows that ventricles grow in size as one ages. This may be explained by the fact that brain naturally atrophies with age, leading to relative enlargement of the ventricles. This information can be used as normal range of ventricle volume for a particular age in a defined gender. Ventricle volume outside this normal range can be indicative of hydrocephalus or a neurodegenerative disease.

While the above plot showed variation of lateral ventricle volumes across age and sex, it might be easier to visualize relative proportion of lateral ventricles compared to cranial vault volume. This also has a normalizing effect across sexes; difference in ventricular volumes between sexes might be due to difference in cranial vault sizes.

relative lateral ventricular volume variation

This plot looks similar to the plot before, with the ratio of the ventricular volume to the cranial vault increasing with age. Until the age of 30-35, males and females have relatively similar ventricular volumes. After that age, however, males tend to larger relative ventricular size compared to females. This is in line with prior research which found that males are more susceptible to atrophy than females[10].

We can incorporate all this analysis into our automated report. For example, following is the CT scan of an 75 year old patient and our automated report.


CT scan of a 75 Y/M patient.
Use scroll bar on the right to scroll through slices.

qER Analysis Report

Patient ID: KSA18458
Patient Age: 75Y
Patient Sex: M

Preliminary Findings by Automated Analysis:

- Infarct of 0.86 ml in left occipital region.
- Dilated lateral ventricles.
  This might indicate neurodegenerative disease/hydrocephalus.
  Lateral ventricular volume = 88 ml.
  Interquartile range for male >=75Y patients is 28 - 54 ml.

This is a report of preliminary findings by automated analysis.
Other significant abnormalities may be present.
Please refer to final report.

Our auto generated report. Added text is indicated in bold.


The question of how to establish the ground truth for these measurements still remains to be answered. For this study, we use DICE scores versus manually outlined ventricles as an indicator of segmentation accuracy. Ventricle volumes annotated slice-wise by experts are an insufficient gold-standard not only because of scale, but also because of the lack of precision. The most likely places where these algorithms are likely to fail (and therefore need more testing) are anatomical variants and pathology that might alter the structure of the ventricles. We have tested some common co-occurring pathologies (hemorrhage), but it would be interesting to see how well the method performs on scans with congenital anomalies and other conditions such as subarachnoid cysts (which caused an earlier machine-learning-based algorithm to fail [9]).

  • Recording ventricular volume on reports is a good idea for future reference and monitor ventricular size in individuals with varying pathologies such as traumatic brain injury and colloid cysts of the third ventricle.
  • It provides an objective measure to follow ventricular volumes in patients who have had shunts and can help in identifying shunt failure.
  • Establishing the accuracy of these automated segmentation methods algorithms also paves the way for more nuanced neuroradiology research on a scale that was not previously possible.
  • One can use the data in relation to the cerebral volume and age to define hydrocephalus, atrophy and normal pressure hydrocephalus.


  1. EVANS, WILLIAM A. “An encephalographic ratio for estimating ventricular enlargement and cerebral atrophy.” Archives of Neurology & Psychiatry 47.6 (1942): 931-937.
  2. Matsumae, Mitsunori, et al. “Age-related changes in intracranial compartment volumes in normal adults assessed by magnetic resonance imaging.” Journal of neurosurgery 84.6 (1996): 982-991.
  3. Scahill, Rachael I., et al. “A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging.” Archives of neurology 60.7 (2003): 989-994.
  4. Hanson, J., B. Levander, and B. Liliequist. “Size of the intracerebral ventricles as measured with computer tomography, encephalography and echoventriculography.” Acta Radiologica. Diagnosis 16.346_suppl (1975): 98-106.
  5. Gyldensted, C. “Measurements of the normal ventricular system and hemispheric sulci of 100 adults with computed tomography.” Neuroradiology 14.4 (1977): 183-192.
  6. Haug, G. “Age and sex dependence of the size of normal ventricles on computed tomography.” Neuroradiology 14.4 (1977): 201-204.
  7. Toma, Ahmed K., et al. “Evans’ index revisited: the need for an alternative in normal pressure hydrocephalus.” Neurosurgery 68.4 (2011): 939-944.
  8. Ambarki, Khalid, et al. “Brain ventricular size in healthy elderly: comparison between Evans index and volume measurement.” Neurosurgery 67.1 (2010): 94-99.
  9. Yepes-Calderon, Fernando, Marvin D. Nelson, and J. Gordon McComb. “Automatically measuring brain ventricular volume within PACS using artificial intelligence.” PloS one 13.3 (2018): e0193152.
  10. Gur, Ruben C., et al. “Gender differences in age effect on brain atrophy measured by magnetic resonance imaging.” Proceedings of the National Academy of Sciences 88.7 (1991): 2845-2849.