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’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 multicentre 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


  • 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. 


Gain CoR endorsed super user training

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

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. 

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


    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.


    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

    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

    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.”