Automated Chest X-ray Interpretation – qXR v2.0

qXR can detect upto 18 different findings with a pre read assistance that that populates radiologist templates, has a worklist tool to segregate Normal and Abnormal Chest X-ray studies, and a proprietary algorithm that looks for signs of Tuberculosis on a Chest X-ray.

Clinical Applications

Pre-populate Chest X-Ray Scan reports

Full text report that pre-fills the the radiology template

Pre-populate Chest X-Ray Scan reports

qXR is a chest X-ray screening tool built with deep learning. It classifies chest X-rays as normal or abnormal, identifies the abnormal findings, and highlights them on the X-ray.

qXR also generates a description of the X-ray findings, including name, size and location of the abnormality, that is used to pre-fill radiology reports.

The algorithms have been trained and tested using a growing database (over 2.5 Million) of X-rays from diverse sources.

qXR is used for TB screening worldwide

qXR being used in a Mobile van for TB screening

qXR is used for TB screening worldwide

The artificial intelligence algorithm underlying qXR is trained to detect not only classical primary pulmonary TB, but also atypical manifestations. It can be used to simultaneously screen for COPD, lung malignancies in high-risk populations, and certain cardiac disorders.

Complimented by an End-to-End software designed for screening programs, qXR for TB can screen results at the Point of Care, so that confirmatory diagnosis and notification of cases can happen on the same day.

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Prioritize abnormal studies on the worklist

AI-generated priority status displayed on the worklist

Prioritize abnormal studies on the worklist

qXR can screen a Chest X-ray and distinguish between Normal and Abnormal scans with a high degree of accuracy.

Integrated with a Radiology worklist, it can facilitate of reporting of all Abnormals first to improve turnaround for cases that need immediate attention.

Product Capability

qXR can detect and localize multiple findings in a Chest X-ray including abnormal classification, different types of lung parenchymal opacities, pneumothorax, pleural effusion, cardiac enlargement, and anatomical variations seen in the chest.

Nodule
Nodule
Multiple opacity
Multiple opacities
Cavity
Cavity
Consolidation
Consolidation
Fibrosis
Fibrosis
Blunted CP
Blunted CP
Pleural Effusion
Pleural Effusion
Hilar Enlargement
Hilar Enlargement
Cardiomegaly
Cardiomegaly
Nodule

Nodule

Detects and localizes different types of Lung parenchymal opacities
Multiple opacity

Multiple opacity

Detects and localizes different types of Lung parenchymal opacities
Cavity

Cavity

Detects and localizes different types of Lung parenchymal opacities
Consolidation

Consolidation

Detects and localizes different types of Lung parenchymal opacities.
Fibrosis

Fibrosis

Detects and localizes different types of Lung parenchymal opacities
Blunted CP

Blunted CP

Detects and localizes subtle blunting of CP angles seen due to small effusions or due to pleural thickening
Pleural Effusion

Pleural Effusion

Detects and localizes pleural effusions marking the affected lung with a bounding box
Hilar Enlargement

Hilar Enlargement

Can identify cases where the Hilum is prominent
Cardiomegaly

Cardiomegaly

Can identify cardiac enlargement in a Chest X-ray.

Validation Studies

Accuracy Rates

The list of abnormalities that the deep learning algorithms for qXR keeps growing and it is now part of multiple independent validation studies. The accuracy of 10 different findings is reported individually below, using majority opinion of 3 radiologists as ground truth. By altering the detection threshold, each algorithm can be operated at either a high-sensitivity or high-specificity operating point depending on the clinical setting.

Abnormal finding AUC (Confidence interval) Operating point 1 (sensitive) Operating point 2 (specific)
Sensitivity Specificity Sensitivity Specificity
Normal(No Abnormality Detected) 0.856 (0.853 - 0.859) 0.90 0.56 0.66 0.90
Blunted CP Angle 0.947 (0.942 - 0.952) 0.90 0.88 0.88 0.90
Cardiomegaly 0.95 (0.947 - 0.954) 0.90 0.89 0.85 0.90
Cavity 0.964 (0.948 - 0.980) 0.93 0.90 0.90 0.93
Consolidation 0.941 (0.934 - 0.948) 0.90 0.88 0.87 0.90
Fibrosis 0.937 (0.928 - 0.946) 0.90 0.83 0.85 0.90
Hilar Enlargement 0.844 (0.828 - 0.860) 0.90 0.57 0.58 0.90
Nodule 0.920 (0.909 - 0.931) 0.90 0.77 0.80 0.90
Opacity 0.936 (0.933 - 0.939) 0.90 0.82 0.84 0.90
Pleural Effusion 0.957 (0.953 - 0.960) 0.90 0.88 0.88 0.90
Pneumothorax 0.967 (0.950 - 0.983) 0.95 0.85 0.90 0.90

Get in Touch

Write to us at partner@qure.ai to request a demo.