In Focus Uncategorized

Need for Speed: AI, AstraZeneca, and early lung cancer diagnosis

The AstraZeneca-Qure partnership

A thousand miles begins with a single step. In 2020, and AstraZeneca took the first step together to integrate advanced artificial intelligence (AI) solutions to identify lung diseases early in patients across AstraZeneca’s Emerging Markets region – Latin America, Asia, Africa, and the Middle East. In the past 2 years, the partnership has made significant progress, incorporating the use of AI technology with chest X-rays for multi-disease screening, including tuberculosis and heart failure along with lung cancer.

Lung Cancer: The need for early detection

In more than 40% of people suffering from Lung Cancer, it is detected at Stage 4, when their likelihood of surviving 5 years is under 10%. Only 20% are diagnosed at Stage 1 when the survival rate is between 68-92%. That’s why Lung Cancer is responsible for every 1 in 5 cancer deaths worldwide.

Though early detection facilitates early diagnosis and better patient outcomes, the disease’s silent progress to advanced stages makes it a challenge like none other. Low Dose CT (LDCT) remains the most effective means of screening for Lung Cancer. However, in LMICs, CTs can be prohibitively expensive, priced between USD 500 – 700, limiting their access. However, there is some hope.

Chest X-rays are one the most routinely performed exams in the world, representing 40% of the approximately 3.6 billion imaging tests that are performed annually. As a non-invasive diagnostic test with easy access and low costs, the chest X-ray is a valuable first line test to screen for radiological indications of issues in the lungs, heart, ribs, and more. Acquiring chest X-ray scans only takes minutes; but it warrants expert radiologists to read and analyze them.

Augmenting X-rays with the power AI

qXR,'s AI-powered chest X-ray interpretation tool, can automatically detect and localize up to 30 abnormalities, including indicators of Lung Cancer, TB, and COVID-19. This is particularly impactful when millions of scans are examined using qXR to report any abnormalities that could otherwise be missed due to:

  • Lack of experienced personnel
  • Increased workloads, limiting access and time for detailed reads of abnormal scans
  • Incidental nodules indicative for Lung Cancer being missed because physicians are only looking at the results for which the X-rays were ordered and not incidental findings.

How Qure is making a difference

1. Working with grassroot level healthcare professionals

A. Leveraging Primary Care GP clinics in Malaysia

Qualitas Medical Group (QMG) is a chain of integrated general practice (GP) clinics, dental clinics, medical imaging centers, and ambulatory care management centers that play an integral role in Malaysia’s health system. Along with Lung Cancer Network Malaysia, QMG uses qXR to triage all chest X-rays taken of local workers, identifying incidental lung nodules that maybe indicative of lung cancer for further testing. qXR has also helped GPs to reduce their dependency on radiologists for second reads and reduced reporting turnaround time for chest X-rays from 2 days to the same day.

“’s state of the art deep learning technology is a potential game changer that will enhance and expedite diagnosis with rapid referral to relevant specialty “, said Dr Anand Sachithanandan, President, Lung Cancer Network Malaysia

B. Empowering Primary Care Physicians in Latin America

Primary care centers are the first medical care touchpoint and are crucial stakeholders for early diagnosis in disease care pathways. In collaboration with Lung Ambition Alliance, Latin America, Qure is empowering primary care physicians in 12 different countries with AI-enabled smart phone-based chest X-ray analysis and lung nodule screening.

In the absence of digital X-rays, physicians only need to click a picture of the X-ray film against a lightbox and upload it on the app to receive instant qXR analysis. Based on the results, they can guide the patient to the next appropriate steps.

2. Collaborating with Cancer Care Foundations

Assam is called India’s Cancer Capital as the state’s average cancer incident rate is double the national average. The high cancer burden, low public awareness, and a lack of specialised health-care infrastructure led the Govt. of Assam to partner with Tata Trusts and build the Assam Cancer Care Foundation (ACCF).

Potential lung cancer suspects are identified via door-to-door screenings as well as via a screening kiosk set up at the Fakhruddin Ali Ahmed Medical College and Hospital, Barpeta where ACCF have built a specialised cancer care unit. Chest X-rays of these individuals will be screened for detection of suspicious lung nodule(s) using qXR. Based on the result, they will either be called back for an LDCT/Biopsy or an oncology consultation.

3. Surveillance of all chest X-rays taken in a tertiary care hospital

The VPS Lakeshore, Kerala is a tertiary care hospital and a centre of excellence in Oncology and other specialities. It is well equipped to take up largescale screening programs and facilitate the required care continuum for high risk, suspected and confirmed disease cases. The hospital has a program in place where a tool surveys all chest X-rays taken to facilitate early detection of Lung Cancer.

Through our partnership with AstraZeneca, we have deployed qXR to scan all chest X-rays performed at the hospital to pick up possibly early cases of Lung Cancer. If any abnormal/nodule indicative cases are picked up by the software, it is instantly flagged to the radiologist/referring physician so that they can guide the patient along the next steps in the patient care pathway.

4. Public screening road shows

The Ministry Of Public Health, Thailand along with the AstraZeneca team initiated the “Don’t Wait. Get Checked” Lung Cancer Campaign in April ’22 at Central World Mall, in partnership with Banphaeo General Hospital, Digital Economy Promotion Agency (DEPA) and the Central Group. On the occasion of World No Tobacco Day,’s qXR was used to screen close to 200 people. The objective of this program was to directly impact Thailand's public health policies revolving around lung cancer.

Way Forward

“Building health systems that are resilient and sustainable will require finding new ways to prevent disease, diagnose patients earlier, and treat them more effectively. The benefits of the technology that offers align well with our corporate values, ultimately supporting our strategic objective to reshape healthcare delivery, close the cancer care gap and better chronic disease management, especially in low-to-middle income countries. We believe that innovative technology has the potential to transform patients’ outcomes, enabling more people to access care in timely, reliable and affordable ways, regardless of where they live”, said Pei-Chieh Fong, Medical VP, AstraZeneca International.

At the Davos World Economic Forum 2022, AstraZeneca pledged to join the WEF EDISON Alliance and committed to screening 5 million patients for lung cancer by 2025 in partnership with

With the support of AstraZeneca Turkey, collaborated with Mersin University Hospital on a landmark study for the use of AI in Heart Failure detection, using our qXR suite. This study is an important indicator for the future of AI in healthcare and the use of technology to augment the efforts of physicians in the early detection of other diseases.

In Focus Uncategorized

Aarthi Scans: Scripting tele-radiology growth in India

The rapidly increasing need for radiology diagnostic and image interpretation services around the world has brought two major issues to light. The first is the lack of radiologists and the other is the dearth of specialized knowledge.

Building reliable communication and image transfer systems to tap into the expertise of radiologists who are not on-site can solve these issues to some extent. Hospitals, mobile imaging firms, urgent care clinics, and even some private practices all around the world are increasingly using tele-radiology. Tele-radiology improves patient care by allowing radiologists to provide services without having to be physically present at the imaging site so that the patients can receive round-the-clock access to trained specialists.

Tele-radiology is significantly less expensive than having a radiologist on-site. These services are typically priced per exam, with the cost as low as $1 per X-ray Tele-radiology has transformed the practice of many radiology clinics around the world, allowing them to provide results faster and by facilitating access to the radiologist, adding enormous value to the diagnostic process.

To set up a tele-radiology system between two centres, one requiring radiologist’s services and one providing radiologist’s services, the following elements are needed:

  1. Modality – a system that captures the medical image and has the facility to send these images in the preferred format i.e., DICOM
  2. PACS – a system that stores, sends, and receives medical images (DICOMs) and that can be identifiable by a unique address (like IP address, port number, etc.)
  3. Gateway – a medium that handles communication between the two centers (source and the destination) – receives the medical images from the source and sends back the output in the required format) using API calls. Complying with the data security and Protected Health Information (PHI) standards, de-identification and re-identification of confidential information can be performed by the Gateway.
  4. API Hub – a single place where all the Application Programming Interfaces (APIs) can be published and shared with external parties/clients by the intended service provider (tele-radiology) com

Introducing Aarthi Scans: India’s largest Tele-radiology service provider

India with its population of around 1.44 billion, right now we have around 1 radiologist for 100,000 people. Most rural India still lacks adequate radiological services and personnel, and not all imaging centers have subspecialty expertise, tele-radiology plays a significant role in quality diagnostics.

Aarthi Scans and Labs, one of the largest diagnostic centers were started in the year 2000 by Mr. Govindarajan. Today Aarthi Scans has more than 100+ branches across 10 states.It was in 2011 that they started their tele-radiology services to provide quick reporting for emergency cases and nighttime reporting and to ensure continuous reporting even when radiologists go on leave. Their radiologists' review over 200,000 CTs, 250,000 MRIs, and 2.1 million chest X-rays annually.

“At that point of time in 2011, when we started tele-radiology, telemedicine as a concept had not evolved in India. Radiologists giving reports without being present at the scanning location was viewed with skepticism. But once the referring doctors started viewing the benefits of tele-radiology, like nighttime reports, subspecialty reporting, they were impressed. Radiologists also needed a lot of convincing to report tele-radiology images. We standardised and digitised patient history, records, improved communication channels between tele-radiologists and radiographer in scanning sites and there was a slow and steady adoption by the Radiologist community. Our PACS vendor – Mr Ravindran from Innowave Healthcare Technologies helped us a great deal in solving the workflow related issues and helping us choose the right technology for us.”

 Govindarajan, Aarthi Scans and Labs

Aarthi Scans has taken a step forward by incorporating Artificial Intelligence (AI) into their reporting procedure, demonstrating their commitment to staying on the cutting edge of technology. The ratio of one radiologist to more than 100,000 people in India has resulted from stress in radiology reporting, scan misreads and reporting delays. Any solution that might assist radiologists to relax and improve their productivity is always welcome at the technologically advanced setup at Aarthi Scans, and AI can be of immense value add in this scenario.'s qXR, a Chest X-ray interpretation software – a CE Class II certified product – has been installed in Aarthi Scans diagnostic centers. The most common application of qXR in this setting is for Radiologist assistance to triage any scans with abnormalities on the worklist. The images are scanned and interpreted in under a minute. All scans that qXR identifies some findings in, are saved as a draft in the radiology worklist for further assessment and reporting. The report is generated in a natural language, significantly reducing the typing time that constitutes a significant portion of the reporting time. The final report is released in 30% lesser time due to this triaging mechanism and reading assistance by qXR. is a leading solution provider and we validated a few solutions before choose  We chose qXR because the accuracy in categorising a study as normal or abnormal is very high (95%)!”.  “We have been using qXRin our day-to-day radiological practice across India in all our branches. We are huge fans of qXR's accuracy and utility.”

– Dr. Arunkumar Govindarajan, Director, Aarthi Scans and Labs

Technical Integration

Qure PACS gateway for acquiring the Chest X-rays is integrated with Freedom Nano PACS which is present in every center of Aarthi Scans. Each center is authenticated with a unique token for the transmission of the studies and their corresponding results. The end-to-end transmission is supported by API calls that communicate with the Qure API hub to send the studies to the qXR AI models for processing. The AI interpretations are sent back to Freedom Nano PACS, where the radiologist can view the result from the individual centers. API and back to Freedom Nano PACS, where the radiologist can view the result from the individual centers. API and https-based communication make the data secured even on the cloud.

Since partnering with Aarthi Scans four months ago, qXR has processed over 45,000 scans, triaging 55% of scans with abnormal findings. On a daily basis, qXR processes 200 chest X-rays.

Scans that are classified as normal by qXR can be evaluated by the radiologist more quickly, giving them more time to review the abnormal scans and cutting down on overall reporting time. This has led to a reduction in the TAT by 30%. The qXR Secondary Capture output also uses contours to localize anomalies in the lungs, enabling radiologists to recognize abnormalities more accurately, without spending as much time as a regular scan.

Finalising a Chest X-ray report as 'normal' is like passing through the valley of uncertainty for every Radiologist and qXR is like that friendly colleague who assists you with a second opinion / confirmation without bias. qXR saves our Radiologists' time and removes doubt while reporting.qXR has resulted in a 30% reduction in reporting time for our Radiologists.

– Dr. Aarthi, Director, Aarthi Scans and Labs

Insights into incorporating AI into the practice – Dr. Arunkumar Govindarajan

“Learning about basics of Artificial Intelligence (AI) has helped me a lot to understand the inner workings of AI and terminologies. To start and understand in deep about AI one can take Coursera courses like – “AI for Everyone" by Andrew Ng and “AI for Medical Diagnosis” by DeepLearningAI. There are a lot of AI solutions out there, one can research in google to find which will suit your patients' and radiologist's needs. Once you fix a good AI vendor –

  • do your own validation and provide transparent feedback
  • partner with a good IT & PACS vendor and fix a workflow suited to your organization's needs. AI integration into PACS takes a bit of effort from the AI and PACS vendor. Be present during the meetings to ease the process and quickly resolve doubts”

What's Next?

After successful operations with qXR in all our centers, we will be next deploying Qure’s AI solution, qER for detecting brain abnormalities from head CT scans.

“Time is Brain and quick qER report never goes in vain” Dr. Arunkumar Govindarajan, Director, Aarthi Scans and Labs


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


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.


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.


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

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