Automated Chest X-ray Interpretation – qXR
qXR v3.0
qXR can detect abnormal findings on a chest X-ray. It can be used to separate normal from abnormal X-rays, for pre-read assistance, or as a radiology audit tool. qXR includes patented algorithms that can detect a total of 29 findings on the chest X-ray.
qXR for Healthcare Providers
Sort chest x-rays in the Worklist
Sort chest x-rays in the Worklist
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.
The CE certified algorithms have been trained and tested using a growing database (over 3.6 Million) of X-rays from diverse sources.
Opt for an AI assisted workflow
Opt for an AI assisted workflow
qXR can detect upto 29 different findings on chest X-rays. Irrespective of CR/DR scans or PA/AP views, qXR can aid in detecting multiple findings with the lungs, pleura, heart, bones and the diaphragm.
The algorithms generate contours for lung and pleural abnormalities for quick and easy interpretation. Integrated with multiple PACS providers globally, qXR outputs are pushed back in under a minute for each scan.
Reduce reporting turnaround with pre-populated templates
Reduce reporting turnaround with pre-populated templates
qXR can generate free text reports that can be pushed back as Structured DICOM reports for immediate adoption in the workflow.
Currently, qXR uses globally compliant templates which can be tailored based on hospital requirements. Each scan analysed by qXR can also display a sidebar for all the findings presented in a dichotomized fashion.
Product Capability
qXR can detect and localize multiple findings in a Chest X-ray including detection of multiple findings with the lungs, pleura, heart, bones and the diaphragm.
Nodule
Detects and localizes different types of Lung parenchymal opacities
Multiple opacities
Detects and localizes different types of Lung parenchymal opacities
Cavity
Detects and localizes different types of Lung parenchymal opacities
Consolidation
Detects and localizes different types of Lung parenchymal opacities.
Fibrosis
Detects and localizes different types of Lung parenchymal opacities
Blunted CP
Detects and localizes subtle blunting of CP angles seen due to small effusions or due to pleural thickening
Pleural Effusion
Can detect and localise small and large pleural effusions.
Hilar Enlargement
Can identify cases where the Hilum is prominent
Nasogastric & Endotracheal Tube Detection
Can detect placement and identify misplaced Breathing tubes and Nasogastric tubes.
Pneumoperitoneum
Can detect and localize free air in the abdomen seen below the diaphragm.
Pneumothorax
Can detect and localize pneumothorax.
Rib Fracture
Can detect and localize rib fractures.Quantification & Progression
With qXR v3.0, the analytical capabilites of the algorithms go a step further. Individual finding specific abnormalities detect and localise the lesion in the lungs and then quantify the affected lesion in comparison to the entire lung volume seen on the chest X-ray.
qXR can also analyse multiple scans from the same patient sequentially and create a progression report to detect changes in lesions over time.
We were impressed with the seamless set-up of the algorithm allowing us to test it with very little effort and get immediate results. Also we realized how easy the algorithms quantification methods make it to follow up with a patients’ improvement or deterioration.
Florentino Bernardo,
CIO, Grupo Empresarial Angeles.
qXR Accolades
First published online on July 2018, the study has been updated to include validation of 10 different findings on the Qure 100k test set and an additional algorithm accuracy comparison against a 3 radiologist majority on 2000 scans.
Read the paper on arXiv
Independent evaluation at Mass General Hospital, Boston, USA on 874 Chest X-rays for 4 different findings. qXR Algorithms were found to outperform Radiologists with different years of experience against Ground Truth set by Thoracic Radiologists and were able to quantify change over time
Read the paper on PLOS ONEDrop us a line at partner@qure.ai to know how we can set up qXR in your imaging workflow.