Author: Pramod Rapeti

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Role of an AI for Reliable Screening of Abnormality in X-rays

Role of an AI for Reliable Screening of Abnormality in X-rays: A Prospective Multicenter Study on Operational Efficiency using a CE approved solution

Chest X-rays are the most common diagnostic imaging technique used in clinical practice. The patient care pathway is, however, significantly hampered in most high-volume healthcare centers. Therefore, an Artificial Intelligence system that can quickly, reliably, and accurately identify anomalies in chest X-rays is being agreed as essential for enhancing radiological workflow.   

Here are some of the key challenges in correctly identifying the abnormalities on a chest X-ray (CXR)  

  • Scarcity of radiologists with the necessary training  
  • Overwhelming workload in large healthcare facilities  
  • CXR interpretations are highly subjective due to the presence of overlapping tissue structures.  

For example: Sometime even well-trained radiologists find it challenging to differentiate between the lesions or correctly identify very obscure pulmonary nodules   

Objective: To conduct a CE-approved post-market study which is also a prospective multi-center and multi-reader study prospective multicenter quality-improvement study. The team evaluated whether artificial intelligence (AI) can be used as a chest X-ray screening tool in real clinical settings. 

Method: A team of expert radiologists used Qure.ai’s CE- approved AI-based chest X-ray screening tool (qXR) as a part of their daily reporting routine to report consecutive chest X-rays for this prospective multi–centre study. This study took place in a large radiology network in India for a period of 10 months. This was done is over 35 + sites by ~120 expert radiologists.  

Study Highlights

  • A total of 65,604 chest X-rays (CXRs) were processed from a network of 35 centers during the study period. 
  • Turnaround Time (TAT) decreased by about 40.63% from pre-AI period to post-AI period. 
  • The high NPV (98.9%) in categorizing normal and abnormal CXR with confidence demonstrates the utility of Qure’s AI as a screening tool in high-volume settings. 
  • The 1.1% missed CXRs were non-critical and non-actionable x-rays which don't need follow-up. 

Investigator Comments

“AI-based chest X-ray solution (qXR) screened chest X-rays and assisted in ruling out normal patients with high confidence, thus allowing the radiologists to focus more on assessing pathology on abnormal chest X-rays and treatment pathways.” 

“qXR helped decrease the mean TAT by over 40%, and 99% of the AI reported normal CXRs were actually normal.” 

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

Conclusion

  • The study has prospectively demonstrated that using AI as an assistance tool can be beneficial in high-workload healthcare facilities. 
  • The Study showcased how AI can shorten the time patients must wait to receive the final report, particularly in normal circumstances. Normal and abnormal CXR are on the same worklist in a typical system, making it impossible to separate or prioritize normal CXR without opening the CXR.  
  • AI can reduce report errors and missed diagnoses by acting as a secondary reader. 
  • The use of AI will result in more appropriate treatments for the disease beyond a reduction in reporting time and an improvement in report quality. 

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Gain CoR endorsed super user training

CoR endorsed CPD Super User Training by Qure.ai

Qure.ai became the first company to receive an endorsement from The College of Radiographers (CoR) for CPD Super User Training. This training helps in obtaining Qure.ai’s Artificial Intelligence Super User Certificate. This training is a fantastic opportunity to receive and get a CPD Certificate.

Qure.ai is the first AI company in the UK to receive this kind of endorsement.

COR knowledge outcomes are,

[CoR 02] Knowledge base
[CoR 03] Work safely
[CoR 06] Manage knowledge/information
[CoR 09] Interprofessional/agency working or learning
[CoR 11] Workforce development or staff governance

What is CPD and how does it help:

Continuing Professional Development (CPD) is to help improve the safety and quality of care provided for patients and the public in the UK. 

CPD Helps
-in updating the latest changes in medical practices. 
-in maintaining the professional standards required. 
-in annual appraisal to show that one has met the requirements for revalidation.

Health professionals are responsible for:

  1. identifying their CPD needs
  2. undertaking CPD activities that are relevant to their practice and support professional development 
  3.  

CPD should be focussed on four primary domains: 

  1. knowledge, skills, and performance  
  2. safety and quality  
  3. communication, partnership, and teamwork  
  4. maintaining trust 

Evidence of CPD is vital in the annual appraisals. A CPD portfolio would typically include a selection of activities in at least 3 of the following categories: 

  1. Work-based 
  2. Professional 
  3. Formal 
  4. Self-directed 
  5. Other learning 

Healthcare professionals must undertake 35 hours of Continuing Professional Development (CPD) relevant to their scope of practice over the three years before their registration renewal. 

Who are all eligible for this training

All medical professionals registered under The Health and Care Professionals Council are eligible for this training

  1. Maintain a continuous, up-to-date, and accurate record of their CPD activities. 
  2. Demonstrate that their CPD activities are a mixture of learning activities relevant to current or future practice. 
  3. Seek to ensure that their CPD has contributed to the quality of their practice and service delivery. 
  4. Seek to ensure that their CPD benefits the service user. 
  5. Upon request, present a written profile (which must be their work and supported by evidence) explaining how they have met the Standards for CPD. 

For more details, reach out to us

    Qure.ai is committed to protecting and respecting your privacy and processes your information as our Privacy Policy.

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    AI-Based Gaze Deviation Detection to Aid LVO Diagnosis in NCCT

    Introduction

    Strokes occur when blood supply to the brain is interrupted or reduced, depriving brain tissue of oxygen and nutrients. It is estimated that a patient can lose 1.9 million neurons each minute when a stroke is untreated. So, the treatment of stroke is a medical emergency that requires early intervention to minimize brain damage and complications. Furthermore, a stroke caused by emergent large vessel occlusion (LVO) requires a much more prompt identification to improve clinical outcomes.

    Neuro interventionalists need to activate their operating rooms to prepare candidates identified for endovascular therapy (EVT) as soon as possible. As a result, identifying imaging findings on non-contrast computed tomography (NCCT) that are predictive of LVO would aid in identifying potential EVT candidates. We present and validate gaze deviation as an indicator to detect LVO using NCCT. In addition, we offer an Artificial Intelligence (AI) algorithm to detect this indicator.

    What is LVO?

    Large vessel occlusion (LVO) stroke is caused by a blockage in one of the following brain vessels:

    1. Internal Carotid Artery (ICA) 
    2. ICA terminus (T-lesion; T occlusion) 
    3. Middle Cerebral Artery (MCA) 
    4. M1 MCA 
    5. Vertebral Artery 
    6. Basilar Artery

    Image source: Science direct

    LVO strokes are considered one of the more severe kinds of strokes, accounting for approximately 24% to 46% of acute ischemic strokes. For this reason, acute LVO stroke patients often need to be treated at comprehensive centers that are equipped to handle LVOs. 

    Endovascular Treatment (EVT)

    EVT is a treatment given to patients with acute ischemic stroke. Using this treatment, clots in large vessels are removed, helping deliver better outcomes. EVT evaluation needs to be done at the earliest for the patients that meet the criteria and are eligible. Early access to EVT increases better outcomes for patients.  The timeframe to perform is usually between 16 – 24 hours in most acute ischemic cases.

    Image Source: PennMedicine

    Goal for EVT

    Since it is important to perform this procedure as early as possible, how do we get there?

    LVO detection on NCCT

    There is a 3 point step to consider for this:

    1. Absence of blood
    2. Hyperdense vessel sign or dot sign
    3. Gaze deviation (often overlooked on NCCT) 

    Gaze deviation and its relationship with acute stroke

    Several studies suggest that gaze deviation is largely associated with the presence of LVO [1,2,3].

    Stroke patients with eye deviation on admission CT have higher rates of disability/death and hemorrhagic transformation. Consistent assessment and documentation of radiological eye deviation on acute stroke CT scan may help with prognostication [4].

    AI algorithm to identify gaze deviation

    We developed an AI algorithm that reports the presence of gaze deviation given an NCCT scan. Such AI algorithms have tremendous potential to aid in this triage process. The AI algorithm was trained using a set of scans to identify gaze direction and midline of the brain. The gaze deviation is calculated by measuring the angle between the gaze direction and the midline of the brain. We used this AI algorithm to identify clinical symptoms of ipsiversive gaze deviation in stroke patients with LVO treated with EVT. The AI algorithm has a sensitivity and specificity of 80.8% and 80.1% to detect LVO using gaze deviation as the sole indicator. The test set had 150 scans with LVO-positive cases where thrombectomy was performed.

    Discussion

    Ipsiversive Gaze deviation on NCCT is a good predictor of LVO due to proximal vessel occlusions in ICA terminus and M1 occlusions. However, it is a poor predictor of LVO due to M2 occlusion. We report an AI algorithm that can identify this clinical sign on NCCT. These findings can aid in the triage of LVO patients and expedite the identification of EVT candidates. 

    We are presenting this AI method at SNIS 2022, Toronto. Please attend our oral presentation on 28th July 2022 at 12:15 PM (Toronto time).

     

    Upadhyay, Ujjwal & Golla, Satish & Kumar, Shubham & Szweda, Kamila & Shahripour, Reza & Tarpley, Jason. (2022). Society of NeuroInterventional Surgery SNIS

    References

    [1] Silva, Ana & Pessoa, Ana & Nogueira, Renato & Araújo, José & Alves, José & Pinho, João & Ferreira, Carla. (2019). Prognostic information of gaze deviation in acute ischemic stroke patients. Neurological Sciences. 41. 10.1007/s10072-019-04140-7.

    [2] McCluskey, G., Hunter, A., Best, E., McKee, J., McCarron, M. O., & McVerry, F. (2019). Radiological Eye Deviation as a Predictor of Large Vessel Occlusion in Acute Ischaemic Stroke. Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association, 28(8), 2318–2323. https://doi.org/10.1016/j.jstrokecerebrovasdis.2019.05.029 

    [3] Jiang, N. N., Wu, W., Fong, C., Sahlas, D. J., & Larrazabal, R. (2020). Role of Radiological Eye Deviation in Imaging Occult Cases of Acute Large Vessel Occlusion. Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association, 29(5), 104687. https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.104687

    [4] Payabvash, S., I. Qureshi, and A. I. Qureshi. “Clinical implications of eye deviation on admission CT examination of acute ischaemic stroke patients." Clinical Radiology 71.12 (2016): 1314-e11.

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

    The AstraZeneca-Qure partnership

    A thousand miles begins with a single step. In 2020, Qure.ai 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, Qure.ai'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.

    “Qure.ai’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, Qure.ai’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 Qure.ai 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 Qure.ai.

    With the support of AstraZeneca Turkey, Qure.ai 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.

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    Qure’s AI for detecting risk of heart failure

    Every year, approximately 17.9 million lives are lost to cardiovascular diseases (CVD), the leading cause of death across the world. The rates of heart failure misdiagnosis range from 16.1% in hospitals to 68.5% in GP referral settings. In the EU alone, the economic burden of cardiovascular diseases exceeds €210 Bn.

    A systematic analysis on 10 studies done across 5 countries found patients groups with comorbidities and COPD, and the elderly population in nursing homes were more likely to have unrecognized heart failure.

    Turkey is known to have a higher prevalence of heart failure and Atrioventricular Septal Defect (AVSD) as compared to the western world. With millions of chest radiographs done annually for a host of reasons, a tool that could screen the data used in these studies to predict the early signs for risk of heart failure could be ground-breaking for care and patient outcomes.

    Output generated by Chest X-ray AI Solution

    Enlargement of heart in cases of heart failure

    At the start of 2021, the Department of Cardiology at the Mersin University Faculty of Medicine initiated a study under Digital Transformation with Artificial Intelligence in Health with support from AstraZeneca Turkey to use Qure’s AI solutions to understand the role of AI in predicting heart failures early from incidental findings on Chest X-rays. Only patients who were previously not suspected or identified for signs of heart failure were included in the study.

    Post risk assessment using the AI tool on chest radiographs, the department approached at-risk patients for follow-up tests. A larger number of patients were identified as at-risk but since this study was conducted during the pandemic restrictions, not all individuals came back to the hospital for follow-up. Of the high risk patients who came for follow up tests, 86% were identified to be confirmed heart failure patients. These individuals had confirmatory diagnoses with tests such as NT-proBNP and Echocardiography.

    The results of this year-long exercise have the potential to change the use of AI in cardiology altogether.

    Prof. Dr. Ahmet Çelik, President at Heart Failure Working Group of Turkish Society of Cardiology and the Principal Investigator in this research said,

    “In this study, which was carried out for the early diagnosis of heart failure, the power of artificial intelligence to predict heart failure by looking at lung X-rays was realized with a sensitivity of 89.1 percent and a selectivity of 86.4 percent. More importantly 65.3 percent of patients diagnosed with heart failure had Preserved Ejection Fraction Heart Failure which is difficult to diagnose.”

     Qure’s AI solution has been found to have 95%+ sensitivity for both cardiomegaly and pleural effusion. It could potentially be a game-changer as a silent reader, without increasing the work burden on healthcare professionals or adding significant costs by changing care pathways. It could screen all chest radiographs done worldwide on non-suspecting cases adding thousands of undiagnosed cases onto the cardiology risk assessment, diagnosis, and eventually treatment pathway. With a well-thought-through system for detection and diagnosis, this technology could mean more lives saved with minimal additional investment.

    AstraZeneca Middle East and Africa Region Medical Director Dr. Viraj Rajadhyaksha stated,

    “By applying advanced artificial intelligence and machine learning approaches to patients who go to different units for many reasons, this project will enable them to touch the lives of patients who are diagnosed early and to meet the right treatments much earlier. The results of the research have the potential to create an early detection tool for heart failure for the first time in the world.”

    AstraZeneca team aims to expand the project nationally and apply it to every lung x-ray taken. There has been research exploring the possibility of using AI for X-ray-based cardiac failure detection in a study setting. However, the potential impact on patients has not been demonstrated at such a scale before. This opens a world of opportunities for further focussed research evaluations to ascertain protocols of bringing in clinical practice.

    Prof. Dr. Ahmet Çamsari, the Rector of Mersin University is a strong believer in the potential of AI to impact the diagnostic pathway for patients. He said,

    “Our project will be one of the first projects where artificial intelligence is used in the early diagnosis of undiagnosed and suspected heart failure patients in our country and even in the world. In line with the results obtained, we aim to expand the project nationally and apply it to every lung x-ray taken. Again, we hope that these systems can be used in other fields such as radiological oncology and that artificial intelligence projects that touch the lives of patients can be implemented.”

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    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.

    Qure.ai'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.

    Qure.ai is a leading solution provider and we validated a few solutions before choose Qure.ai.  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