image-150-64c8cd514b55a.webp

image-151-64c8cd518a917.webp

group-1000002623-64c8cd4f8d576.webp

image-146-64c8cd50567b2.webp

image-147-64c8cd501e463.webp

image-148-64c8cd50b2c5c.webp

image-149-64c8cd50d627e.webp

BBC-Logo.webp

The-Independent-Logo.webp

Featured in

AI biomarkers, built for real-world impact and clinical trials. Meet us at ASCO 2026 | Booth #IH12.

Qure.ai Recognized Among TIME’s 100 Most Influential Companies of 2025

Qure.ai partners with Microsoft to increase access to its lung cancer detection and management suite in the U.S.

qTrack

Background:&nbsp;The chest radiograph (CXR) is the most frequently performed radiological examination worldwide. The increasing volume of CXRs performed in hospitals causes reporting backlogs and increased waiting times for patients, potentially compromising timely clinical intervention and patient safety. Implementing computer-aided detection (CAD) artificial intelligence (AI) algorithms capable of accurate and rapid CXR reporting could help address such limitations. A novel use for AI reporting is the classification of CXRs as ‘abnormal’ or ‘normal’. This classification could help optimize resource allocation and aid radiologists in managing their time efficiently.

Methods: qXR is a CE-marked computer-aided detection (CAD) software trained on over 4.4 million CXRs. In this retrospective cross-sectional pre-deployment study, we evaluated the performance of qXR in stratifying normal and abnormal CXRs. We analyzed 1040 CXRs from various referral sources, including general practices (GP), Accident and Emergency (A&amp;E) departments, and inpatient (IP) and outpatient (OP) settings at East Kent Hospitals University NHS Foundation Trust. The ground truth for the CXRs was established by assessing the agreement between two senior radiologists.

Results: The CAD software had a sensitivity of 99.7% and a specificity of 67.4%. The sub-group analysis showed no statistically significant difference in performance across healthcare settings, age, gender, and X-ray manufacturer.

Conclusions: The study showed that qXR can accurately stratify CXRs as normal versus abnormal, potentially reducing reporting backlogs and resulting in early patient intervention, which may result in better patient outcomes.

large_Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust.webp

medium_Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust.webp

small_Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust.webp

thumbnail_Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust.webp

Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust.webp

Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays: External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust

Objective: Pneumonia is most prevalent and acute respiratory disorder. Chest radiography is the gold standard to confirm the clinical condition and the progress. The use of AI in diagnostic workflow proved to be useful. In this study, quantitative assessment by the AI device is compared with the qualitative assessment by the radiologists.

Methods: This retrospective cohort study involved 100 patients and 535 chest radiographs of the admitted COVID-19 pneumonia. The patients selected with at least one follow-up chest x-ray. The level of agreement between radiologists &amp; the software were defined, and the patients are classified into three categories: deteriorating, stable, and improving. The minor criteria include respiratory rate ≥ 30 breaths/min, PaO2/FiO2 ratio ≤ 250, t count &lt; 100,000/μl, hypothermia (core temperature &lt; 36°C), hypotension and the major criteria include septic shock, respiratory failure, infection. The other possible co-morbid or demographic factors like age, gender, body mass index, cigarette smoking status, patient’s history of diabetes, chronic obstructive pulmonary disease (COPD), cerebrovascular disease, etc. are also analyzed to further subdivide the groups and study the impact of them on the patients.

Results: Among the patients in this study, 51% had diabetes, 43% had a history of cardiovascular disease and 44.21% were obese. 17 patients had other respiratory complications at baseline.. No obvious trend is noted, and software-radiologist had higher agreement similar to analysis by visit. Analysis of factors related to death showed only age to be associated with mortality. Patients who died were significantly older (59.25 vs 48.26; p 0.001).

Conclusion: The higher agreement was found between the radiologist and the software. The obtained agreement was also consistent even across the subgroups, only patient age appears to be associated with high-risk mortality. Obesity and diabetes were independent predictors of mortality with susceptibility to develop acute respiratory failure and pulmonary embolism.&nbsp;Explore a study comparing AI and radiologist assessments in diagnosing COVID-19 pneumonia. Discover factors influencing patient outcomes.

large_Comparative Assessment of Chest X-ray Interpretations by AI Model and Radiologist Vs Pulmonologist in Predicting the Clinical Status of Covid-19 Pneumonia Patients.webp

medium_Comparative Assessment of Chest X-ray Interpretations by AI Model and Radiologist Vs Pulmonologist in Predicting the Clinical Status of Covid-19 Pneumonia Patients.webp

small_Comparative Assessment of Chest X-ray Interpretations by AI Model and Radiologist Vs Pulmonologist in Predicting the Clinical Status of Covid-19 Pneumonia Patients.webp

thumbnail_Comparative Assessment of Chest X-ray Interpretations by AI Model and Radiologist Vs Pulmonologist in Predicting the Clinical Status of Covid-19 Pneumonia Patients.webp

Comparative Assessment of Chest X-ray Interpretations by AI Model and Radiologist Vs Pulmonologist in Predicting the Clinical Status of Covid-19 Pneumonia Patients.webp

Comparative Assessment of Chest X-ray Interpretations by AI Model and Radiologist Vs Pulmonologist in Predicting the Clinical Status of Covid-19 Pneumonia Patients

Qure.ai's qXR-PTX-PE is a radiological computer-assisted triage and notification software that analyzes adult chest X-ray images for the presence of pre-specified suspected critical findings (pleural effusion and/or pneumothorax).

Qure.ai, a leading innovator in medical imaging AI, announces a pivotal 510(k) FDA clearance for its&nbsp;AI-enabled chest X-ray solution&nbsp;– qXR, under two critical findings. Qure’s qXR has now been cleared to triage pneumothorax (PTX) and pleural effusion (PE), which present severe challenges in emergency rooms and intensive care units (ICUs). The newest clearance is an addition to Qure’s existing FDA-cleared product portfolio that includes qXR-BT for breathing tube placement, qER for emergency room head CT scans, and qER-Quant for head CT quantification software.&nbsp;&nbsp;

qXR has shown exemplary clinical accuracy in triaging pneumothorax (PTX), a condition where air collects in the pleural space, causing the lung to collapse.&nbsp;On the other hand, Pleura Effusion (PE) is where fluid accumulates in the pleural cavity.&nbsp;Performance evaluations reveal that qXR-PTX-PE excels in providing rapid passive notifications, taking an average of 10 seconds to alert healthcare professionals. This impressive speed positions qXR-PTX-PE as an indispensable medical tool designed to inform specialists promptly, especially in critical settings like ICUs and emergency rooms, and to complement the expertise of our trained medical professionals. Utilizing a globally sourced training dataset, qXR integrates into the existing standard of care workflow and serves as a passive notification system for worklist prioritization.&nbsp;&nbsp;

The 510(k) summary explaining qXR-PTX-PE’s performance and clinical benefits is publicly available here in FDA’s database

large_Qure.ai receives 510(k) FDA clearance for its Chest X-ray solution for Pneumothorax and Pleural Effusion, Elevating ICU and ER Care Pathways.webp

medium_Qure.ai receives 510(k) FDA clearance for its Chest X-ray solution for Pneumothorax and Pleural Effusion, Elevating ICU and ER Care Pathways.webp

small_Qure.ai receives 510(k) FDA clearance for its Chest X-ray solution for Pneumothorax and Pleural Effusion, Elevating ICU and ER Care Pathways.webp

thumbnail_Qure.ai receives 510(k) FDA clearance for its Chest X-ray solution for Pneumothorax and Pleural Effusion, Elevating ICU and ER Care Pathways.webp

Qure.ai receives 510(k) FDA clearance for its Chest X-ray solution for Pneumothorax and Pleural Effusion, Elevating ICU and ER Care Pathways.webp

Qure.ai receives 510(k) FDA clearance for its Chest X-ray solution for Pneumothorax and Pleural Effusion, Elevating ICU and ER Care Pathways

According to studies, around 25% of annual diagnostic imaging procedures are for chest X-rays (CXRs). CXRs make up to almost half (44%) of all radiography pictures in the United States alone. Its broad usage in medical practice for a variety of cardiothoracic conditions can be attributed to several aspects, including ease of accessibility, mobility, familiarity, and price (compared to other imaging tests).&nbsp;&nbsp;

CXRs do, however, have a significant misinterpretation rate—up to 30%. In fact, according to earlier investigations, inter-radiologist and physician concordance for CXRs was as low as 78%.&nbsp;&nbsp;

Missed radiography findings can have serious consequences. For instance, take this other study that demonstrates that 19% of lung tumors that appeared as pulmonary nodules on CXRs were overlooked. Such overlooked results may be fatal for both patients.&nbsp;&nbsp;

The goal of this multi-center study was to find out whether a CXR AI algorithm can spot overlooked or mis-labeled CXR findings in radiology reports.&nbsp;

This study took into consideration multi-institutional radiology databases totaling 13 million reports to identify all CXR reports for the past 22 years. The data was collected from centers such as the Massachusetts General Hospital (MGH), Brigham Women Hospital (BWH), Faulkner Health Center (FH), Martha’s Vineyard Hospital (MVH), Salem Hospital (NSMC), Newton-Wellesley Hospital (NWH), and Spaulding Rehabilitation Hospital (SRH).

The study’s primary application relates to the functionality and practical application of&nbsp;Qure’s&nbsp;CXR AI –&nbsp;qXR&nbsp;. A&nbsp;team of expert radiologists used&nbsp;Qure.ai’s&nbsp;CE- approved&nbsp;qXR&nbsp;for this retrospective multi-center&nbsp;study.&nbsp;It was used to investigate if&nbsp;qXR&nbsp;can reduce the frequency of errors in detection and side-labeling of radiographic findings.&nbsp;

The&nbsp;qXR&nbsp;AI&nbsp;algorithm&nbsp;had&nbsp;high sensitivity (96%), specificity (100%), and accuracy (96%) for detecting all missed and mislabeled CXR findings. The AI could accurately detect mislabeled and missed findings.&nbsp;Qure’s&nbsp;qXR&nbsp;can reduce the frequency of errors in detection and side-labeling of radiographic findings.&nbsp;

large_Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings A Multicenter Study.webp

medium_Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings A Multicenter Study.webp

small_Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings A Multicenter Study.webp

thumbnail_Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings A Multicenter Study.webp

Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings A Multicenter Study.webp

Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study

Three and half million anonymous X-rays were gathered from 45 locations worldwide (in-hospital and outpatient settings). qXR was initially trained on this massive dataset. We used an independent dataset of 13,426 chest X-rays from radiologists’ reports. The test data set included 213,459 X-rays chosen at random from a pool of 3.5 million X-rays. The dataset (development) was developed using the remaining X-rays received from the remaining patients.

<a href="https://qure.ai/product/qxr/">qXR</a>&nbsp;is a deep learning algorithm-enabled software that is used to study nodules and malignant nodules on X-rays. We observed moderate to a substantial agreement even when observations were made with normal X-rays.

<a href="https://qure.ai/product/qxr/">qXR</a>&nbsp;presented a high area under the curve (AUC) of 0.99 with a 95% confidence interval calculated with the Clopper–Pearson method. The specificity obtained with&nbsp;<a href="https://qure.ai/product/qxr/">qXR</a>&nbsp;was 0.90, and the sensitivity was 1 at the operating threshold. The sensitivity value of&nbsp;<a href="https://qure.ai/product/qxr/">qXR</a>&nbsp;in detecting nodules was 0.99, and the specificity ranged from 0.87 to 0.92, with AUC ranging between 0.98 and 0.99. The malignant nodules were detected with a sensitivity ranging from 0.95 to 1.00, specificity between 0.96 and 0.99, and AUC from 0.99 to 1. The sensitivity of radiologists 1 and 2 was between 0.74 and 0.76, with a specificity ranging from 0.98 to 0.99. In detecting the malignant nodules, specificity ranged between 0.98 and 0.99, and sensitivity fell between 0.88 and 0.94.

Machine learning model can be used as a passive tool to find incidental cases of lung cancer or as a triaging tool, which accelerate the patient journey through standard care pipeline for lung cancer

large_Identifying malignant nodules on chest X-rays A validation study of radiologist versus artificial intelligence diagnostic accuracy.webp

medium_Identifying malignant nodules on chest X-rays A validation study of radiologist versus artificial intelligence diagnostic accuracy.webp

small_Identifying malignant nodules on chest X-rays A validation study of radiologist versus artificial intelligence diagnostic accuracy.webp

thumbnail_Identifying malignant nodules on chest X-rays A validation study of radiologist versus artificial intelligence diagnostic accuracy.webp

Identifying malignant nodules on chest X-rays A validation study of radiologist versus artificial intelligence diagnostic accuracy.webp

Identifying malignant nodules on chest X-rays: A validation study of radiologist versus artificial intelligence diagnostic accuracy

Deep learning-based radiological image analysis could facilitate use of chest x-rays as a triaging tool for COVID-19 diagnosis in resource-limited settings. This study sought to determine whether a modified commercially available deep learning algorithm (M-qXR) could risk stratify patients with suspected COVID-19 infections.&nbsp;

A dual track clinical validation study was designed to assess the clinical accuracy of M-qXR. The algorithm evaluated all Chest-X-rays (CXRs) performed during the study period for abnormal findings and assigned a COVID-19 risk score. Four independent radiologists served as radiological ground truth. The M-qXR&nbsp;algorithm output was compared against radiological ground truth and summary statistics for prediction accuracy were calculated. In addition, patients who underwent both PCR testing and CXR for suspected COVID-19 infection were included in a co-occurrence matrix to assess the sensitivity and specificity of the M-qXR&nbsp;algorithm.&nbsp;

625 CXRs were included in the clinical validation study. 98% of total interpretations made by M-qXR&nbsp;agreed with ground truth (p = 0.25). M-qXR&nbsp;correctly identified the presence or absence of pulmonary opacities in 94% of CXR interpretations. M-qXR’s&nbsp;sensitivity, specificity, PPV, and NPV for detecting pulmonary opacities were 94%, 95%, 99%, and 88% respectively. M-qXR&nbsp;correctly identified the presence or absence of&nbsp;pulmonary consolidation&nbsp;in 88% of CXR interpretations (p = 0.48). M-qXR’s&nbsp;sensitivity, specificity, PPV, and NPV for detecting pulmonary consolidation were 91%, 84%, 89%, and 86% respectively. Furthermore, 113 PCR-confirmed COVID-19 cases were used to create a co-occurrence matrix between M-qXR’s&nbsp;COVID-19 risk score and COVID-19 PCR test results. The PPV and NPV of a medium to high COVID-19 risk score assigned by M-qXR&nbsp;yielding a positive COVID-19 PCR test result was estimated to be 89.7% and 80.4% respectively.&nbsp;

M-qXR&nbsp;was found to have comparable accuracy to radiological ground truth in detecting radiographic abnormalities on CXR suggestive of COVID-19.&nbsp;

large_Using artificial intelligence to risk stratify COVID19 patients based on chest X-ray findings.webp

medium_Using artificial intelligence to risk stratify COVID19 patients based on chest X-ray findings.webp

small_Using artificial intelligence to risk stratify COVID19 patients based on chest X-ray findings.webp

thumbnail_Using artificial intelligence to risk stratify COVID19 patients based on chest X-ray findings.webp

Using artificial intelligence to risk stratify COVID19 patients based on chest X-ray findings.webp

Using artificial intelligence to risk stratify COVID19 patients based on chest X-ray findings

To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial CXRs from 405 adult patients (mean age 65 ± 16 years) from two sites in the US (Site A) and South Korea (Site B). We recorded information pertaining to patient demographics (age, gender), smoking history, comorbid conditions (such as cancer, cardiovascular and other diseases), vital signs (temperature, oxygen saturation), and available laboratory data (such as WBC count and CRP). Two thoracic radiologists performed the qualitative assessment of all CXRs based on the RALE score for assessing the severity of lung involvement. All CXRs were processed with a commercial AI algorithm to obtain the percentage of the lung affected with findings related to COVID-19 (AI score). Independent t- and chi-square tests were used in addition to multiple logistic regression with Area Under the Curve (AUC) as output for predicting disease outcome and the need for mechanical ventilation. The RALE and AI scores had a strong positive correlation in CXRs from each site (r2 = 0.79–0.86; p &lt; 0.0001). Patients who died or received mechanical ventilation had significantly higher RALE and AI scores than those with recovery or without the need for mechanical ventilation (p &lt; 0.001). Patients with a more substantial difference in baseline and maximum RALE scores and AI scores had a higher prevalence of death and mechanical ventilation (p &lt; 0.001). The addition of patients’ age, gender, WBC count, and peripheral oxygen saturation increased the outcome prediction from 0.87 to 0.94 (95% CI 0.90–0.97) for RALE scores and from 0.82 to 0.91 (95% CI 0.87–0.95) for the AI scores. AI algorithm is as robust a predictor of adverse patient outcome (death or need for mechanical ventilation) as subjective RALE scores in patients with COVID-19 pneumonia.

large_Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation – a cohort study.webp

medium_Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation – a cohort study.webp

small_Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation – a cohort study.webp

thumbnail_Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation – a cohort study.webp

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation – a cohort study.webp

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation – a cohort study

To evaluate whether the initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in patients with COVID-19.

This retrospective single-center study included adult patients presenting to the emergency department (ED) between February 25 and April 9, 2020, with SARS-CoV-2 infection confirmed on real-time reverse transcriptase polymerase chain reaction (RT-PCR). Initial CXRs obtained on ED presentation were evaluated by a deep learning artificial intelligence (AI) system and compared with the Radiographic Assessment of Lung Edema (RALE) score, calculated by two experienced radiologists. Death and critical COVID-19 (admission to intensive care unit (ICU) or deaths occurring before ICU admission) were identified as clinical outcomes. Independent predictors of adverse outcomes were evaluated by multivariate analyses.

Six hundred ninety-seven 697 patients were included in the study: 465 males (66.7%), median age of 62 years (IQR 52–75). Multivariate analyses adjusting for demographics and comorbidities showed that an AI system-based score ≥ 30 on the initial CXR was an independent predictor both for mortality (HR 2.60 (95% CI 1.69 − 3.99; p &lt; 0.001)) and critical COVID-19 (HR 3.40 (95% CI 2.35–4.94; p &lt; 0.001)). Other independent predictors were RALE score, older age, male sex, coronary artery disease, COPD, and neurodegenerative disease.

AI- and radiologist-assessed disease severity scores on CXRs obtained on ED presentation were independent and comparable predictors of adverse outcomes in patients with COVID-19.

large_Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients – analysis of 697 Italian patients.webp

medium_Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients – analysis of 697 Italian patients.webp

small_Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients – analysis of 697 Italian patients.webp

thumbnail_Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients – analysis of 697 Italian patients.webp

Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients – analysis of 697 Italian patients.webp

Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients – analysis of 697 Italian patients

Majority of chest X-rays (CXRs) performed globally are normal radiologists spend significant time ruling out these scans. We present a Deep Learning (DL) model trained for the specific use of classifying CXRs into normal and abnormal, potentially reducing time and cost associated with reporting normal studies.

A DL algorithm trained on 1,150,084 CXRs and their corresponding reports was developed. A retrospectively acquired independent test set of 430 CXRs (285 abnormal, 145 normal) was analyzed by the algorithm classifying each X-Ray as normal or abnormal. Ground truth for the independent test set was established by a sub-specialist chest radiologist with 8 years’ experience by reviewing every Chest X-Ray image with reference to the existing report. Algorithm output was compared against ground truth and summary statistics were calculated.

The algorithm correctly classified 376 (87.44%) CXRs with sensitivity of 97.19% (95% CI – 94.54% to 98.78%) and specificity of 68.28% (95% CI – 60.04% to 75.75%). There were 46 (10.70%) false positives and 8 (1.86%) false negatives (FNs). Out of 8 FNs, 3 were designated as clinically insignificant (mild, inactive fibrosis) and 5 as significant (rib fractures, pneumothorax).

High-sensitivity DL algorithms can potentially be deployed for primary read of CXRs enabling radiologists to spend appropriate time on abnormal cases, saving time and thereby cost of reporting CXRs, especially on non-emergency situations, More in-depth prospective trials are required to ascertain the overall impact of such algorithms.

large_Automated classification of X-rays as normalAbnormal using a high sensitivity deep learning algorithm.webp

medium_Automated classification of X-rays as normalAbnormal using a high sensitivity deep learning algorithm.webp

small_Automated classification of X-rays as normalAbnormal using a high sensitivity deep learning algorithm.webp

thumbnail_Automated classification of X-rays as normalAbnormal using a high sensitivity deep learning algorithm.webp

Automated classification of X-rays as normalAbnormal using a high sensitivity deep learning algorithm.webp

Automated classification of X-rays as normal/Abnormal using a high sensitivity deep learning algorithm

Deep learning (DL) based solutions have been proposed for interpretation of several imaging modalities including radiography, CT, and MR. For chest radiographs, DL algorithms have found success in the evaluation of abnormalities such as lung nodules, pulmonary tuberculosis, cystic fibrosis, pneumoconiosis, and location of peripherally inserted central catheters. Chest radiography represents the most commonly performed radiological test for a multitude of non-emergent and emergent clinical indications. This study aims to assess accuracy of deep learning (DL) algorithm for detection of abnormalities on routine frontal chest radiographs (CXR), and assessment of stability or change in findings over serial radiographs.

We processed 874 de-identified frontal CXR from 724 adult patients (&gt; 18 years) with DL (Qure AI). Scores and prediction statistics from DL were generated and recorded for the presence of pulmonary opacities, pleural effusions, hilar prominence, and enlarged cardiac silhouette. To establish a standard of reference (SOR), two thoracic radiologists assessed all CXR for these abnormalities. Four other radiologists (test radiologists), unaware of SOR and DL findings, independently assessed the presence of radiographic abnormalities. A total 724 radiographs were assessed for detection of findings. A subset of 150 radiographs with follow up examinations was used to asses change over time. Data were analyzed with receiver operating characteristics analyses and post-hoc power analysis.

About 42% (305/ 724) CXR had no findings according to SOR; single and multiple abnormalities were seen in 23% (168/724) and 35% (251/724) of CXR. There was no statistical difference between DL and SOR for all abnormalities (p = 0.2–0.8). The area under the curve (AUC) for DL and test radiologists ranged between 0.837–0.929 and 0.693–0.923, respectively. DL had lowest AUC (0.758) for assessing changes in pulmonary opacities over follow up CXR. Presence of chest wall implanted devices negatively affected the accuracy of DL algorithm for evaluation of pulmonary and hilar abnormalities.

DL algorithm can aid in interpretation of CXR findings and their stability over follow up CXR. However, in its present version, it is unlikely to replace radiologists due to its limited specificity for categorizing specific findings.

large_Deep learning in chest radiography Detection of findings and presence of change.webp

medium_Deep learning in chest radiography Detection of findings and presence of change.webp

small_Deep learning in chest radiography Detection of findings and presence of change.webp

thumbnail_Deep learning in chest radiography Detection of findings and presence of change.webp

Deep learning in chest radiography Detection of findings and presence of change.webp

Deep learning in chest radiography: Detection of findings and presence of change

Chest x-rays are the most commonly performed, cost-effective diagnostic imaging tests ordered by physicians. A clinically validated, automated artificial intelligence system that can reliably separate normal from abnormal would be invaluable in addressing the problem of reporting backlogs and the lack of radiologists in low-resource settings. The aim of this study was to develop and validate a deep learning system to detect chest x-ray abnormalities.

A deep learning system was trained on 1.2 million x-rays and their corresponding radiology reports to identify abnormal x-rays and the following specific abnormalities: blunted costophrenic angle, calcification, cardiomegaly, cavity, consolidation, fibrosis, hilar enlargement, opacity and pleural effusion. The system was tested versus a 3-radiologist majority on an independent, retrospectively collected de-identified set of 2000 x-rays. The primary accuracy measure was area under the ROC curve (AUC), estimated separately for each abnormality as well as for normal versus abnormal reports.

The deep learning system demonstrated an AUC of 0.93(CI 0.92-0.94) for detection of abnormal scans, and AUC(CI) of 0.94(0.92-0.97),0.88(0.85-0.91), 0.97(0.95-0.99), 0.92(0.82-1), 0.94(0.91-0.97), 0.92(0.88-0.95), 0.89(0.84-0.94), 0.93(0.92-0.95), 0.98(0.97-1), 0.93(0.0.87-0.99) for the detection of blunted CP angle, calcification, cardiomegaly, cavity, consolidation, fibrosis,hilar enlargement, opacity and pleural effusion respectively.

Our study shows that a deep learning algorithm trained on a large quantity of labelled data can accurately detect abnormalities on chest x-rays. As these systems further increase in accuracy, the feasibility of using artificial intelligence to extend the reach of chest x-ray interpretation and improve reporting efficiency will increase in tandem.

large_Can Artificial Intelligence Reliably Report Chest X-Rays Radiologist Validation of an Algorithm trained on 1.2 Million X-Rays.webp

medium_Can Artificial Intelligence Reliably Report Chest X-Rays Radiologist Validation of an Algorithm trained on 1.2 Million X-Rays.webp

small_Can Artificial Intelligence Reliably Report Chest X-Rays Radiologist Validation of an Algorithm trained on 1.2 Million X-Rays.webp

thumbnail_Can Artificial Intelligence Reliably Report Chest X-Rays Radiologist Validation of an Algorithm trained on 1.2 Million X-Rays.webp

Can Artificial Intelligence Reliably Report Chest X-Rays Radiologist Validation of an Algorithm trained on 1.2 Million X-Rays.webp

Can Artificial Intelligence Reliably Report Chest X-Rays? Radiologist Validation of an Algorithm trained on 1.2 Million X-Rays

Chest x-rays are widely used to identify pulmonary consolidation because they are highly accessible, cheap and sensitive. Automating the diagnosis in chest x-rays can reduce diagnostic delay, especially in resource-limited settings.

Anonymised dataset of 423,218 chest x-rays with corresponding reports (collected from 166 centres across India spanning 22 x-ray machine variants from 9 manufacturers) is used for training and validation. x-rays with consolidation are identified from their reports using natural language processing techniques. Images are preprocessed to a standard size and normalised to remove source dependency. These images are trained using deep residual neural networks. Multiple models are trained on various selective subsets of the dataset along with one model trained on entire data set. Scores yielded by each of these models is passed through a 2-layer neural network to generate final probabilities for presence of consolidation in an x-ray.

The model is validated and tested on a test dataset that is uniformly sampled from the parent dataset without any exclusion criteria. Sensitivity and specificity for the tag has been observed as 0.81 and 0.80, respectively. Area under the Receiver Operating Curve (AUC-ROC) was observed as 0.88.

Deep learning can be used to diagnose pulmonary consolidation in chest x-rays with models trained on a generalised dataset with samples from multiple demographics. This model performs better than a model trained on controlled dataset and is suited for a real world setting where x-ray quality may not be consistent.

large_Identifying pulmonary consolidation in Chest X Rays using deep learning.webp

medium_Identifying pulmonary consolidation in Chest X Rays using deep learning.webp

small_Identifying pulmonary consolidation in Chest X Rays using deep learning.webp

thumbnail_Identifying pulmonary consolidation in Chest X Rays using deep learning.webp

Identifying pulmonary consolidation in Chest X Rays using deep learning.webp

Identifying pulmonary consolidation in Chest X Rays using deep learning

For radiologists to develop confidence in a deep learning diagnostic algorithm, it is essential that the algorithm be able to visually demonstrate the evidence for the diagnosis or disease tag. We describe the development of a method that highlights the region(s) of a chest X-ray (CXR) responsible for a deep learning algorithm diagnosis.

Using 24,384 CXRs, we trained 18-layer deep residual convolutional neural networks to predict if a chest X-ray was normal or abnormal, and to detect the presence of ‘cardiomegaly, ‘opacity’, and ‘pleural effusion’ in a CXR. We then applied a method called prediction difference analysis for visualization and interpretation of the trained models. The contribution of each patch in the image is estimated as the degree by which the prediction changes if that patch is replaced with an average normal patch. This method was used to generate a relevance score for each pixel which is consequently visualized as a heat map.

We used a 60-20-20 split for train, validation and test sets. The trained neural network showed an area under the ROC curve of 0.89, 0.92, 0.84, 0.91 for tagging abnormal, cardiomegaly, opacity and pleural effusion respectively on the test set. The visualization pipeline is used to generate heatmaps highlighting the enlarged heart, opacities and the fluid corresponding to the cardiomegaly, opacity and pleural effusion tags.

We trained and tested a deep learning algorithm which accurately classifies and assigns clinically relevant tags to CXRs. Further, we applied a visualization method that generates heatmaps highlighting the most relevant parts of the CXR. The visualization method is broadly applicable to other kinds of X-rays, and to other deep learning algorithms. Future work will focus on formally validating the accuracy of the visualization, by measuring overlap between radiologist annotation and algorithm-generated heatmap.

Heatmaps highlighting evidence for disease tags will provide clinical users with crucial visual cues that could ease their decision to accept or reject a deep learning based chest x-ray diagnosis.

large_Generating Heatmaps to Visualize the Evidence of Deep Learning Based Diagnosis of Chest X-Rays.webp

medium_Generating Heatmaps to Visualize the Evidence of Deep Learning Based Diagnosis of Chest X-Rays.webp

small_Generating Heatmaps to Visualize the Evidence of Deep Learning Based Diagnosis of Chest X-Rays.webp

thumbnail_Generating Heatmaps to Visualize the Evidence of Deep Learning Based Diagnosis of Chest X-Rays.webp

Generating Heatmaps to Visualize the Evidence of Deep Learning Based Diagnosis of Chest X-Rays.webp

Generating Heatmaps to Visualize the Evidence of Deep Learning Based Diagnosis of Chest X-Rays

Our mission is to use artificial intelligence to make Healthcare more affordable and accessible. Learn more about Qure here.

About Us | Qure AI | Learn more about us here

Join the conversation on AI in healthcare with Qure.ai's blog. Get expert insights, tips, and the latest trends shaping the future of medical technology.

Expert AI Healthcare Blog: Insights, Tips & Trends | Qure.ai

To know more about our AI solutions that are helping provide accessible and affordable healthcare, write to us at partner@qure.ai.

Contact Us | Qure AI

Dive into the world of evidence-based medical AI with Qure.ai. Our research and case studies showcase the effectiveness and advancement of AI in healthcare.

Evidence-Based AI in Medicine: Research & Case Studies | Qure.ai

Qure ai is on a mission to provide accessible, affordable, &amp; timely assistance to healthcare professionals across the globe with the help of AI &amp; deep learning

Qure AI | AI assistance for Accelerated Healthcare

Learn about Qure.ai's impact on healthcare through AI innovations. Read how our solutions are making a difference in patient outcomes globally.

Transforming Healthcare: Our Impact with AI Solutions | Qure.ai

Explore the latest in AI healthcare technology with Qure.ai's insights. Discover how artificial intelligence is improving diagnostics and patient care.

Innovative AI Healthcare Insights & Trends | Qure.ai

Legal Universal Terms Data Processing Agreement Legal Disclaimer Acceptable Use Policy V2.0.0 dt: 30.09.2022 V1.1.0 dt: 30.06.2022 UNIVERSAL TERMS (v2.0.0 dated 30.09.2022) YOUR RELATIONSHIP WITH QURE Your use of Qure’s products, software (including but not limited to any and all versions, forms or renditions of qXR, qER, qCT-Lung, qVH, qMSK qTrack), applications such as the

Legal | Qure AI

Stay updated with the latest news and developments in AI-driven healthcare from Qure.ai. Read about our breakthroughs, events, and industry recognition.

Latest AI Healthcare News & Updates | Qure.ai

Privacy Policy Your privacy is important to us. Privacy Notice Effective Date: 1st September 2022 This Privacy Policy explains how we use the personal information that Qure.ai Technologies Private Limited, its subsidiaries and affiliates collects or generates both in relation to our Website https://qure.ai/ along with any webpages and portals thereof and our mobile application

Evidence (Publications & White Papers)