FDA Clears Radiology Device for Detecting Lung Nodules in Chest X-Rays

The software was developed using curated training data from across the world.

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IMIDEX has received FDA clearance for its flagship VisiRad XR product. VisiRad XR is an AI-powered software that analyzes chest X-rays and highlights possible lung nodules and masses. The software was developed using curated training data from across the world. VisiRad XR gives clinicians a closer look at often-overlooked lung lesions, potentially enabling identification of future lung cancers in outpatient and emergency department settings.

VisiRad XR provides a turnkey solution that plugs in to the current radiology workflow. The software routes images with AI-detected lesions back to radiologists within their native viewing environment, allowing them to see VisiRad XR’s results right behind the primary image for interpretation. Requiring no additional testing orders, VisiRad XR seeks to increase the number of detected lung nodules and masses in patients seen through routine care.

IMIDEX conducted two large-scale studies on VisiRad XR that it submitted to the FDA as the heart of its 510(k) submission: a standalone performance study and a multi-center clinical validation study.

In the retrospective standalone study performed by IMIDEX utilizing over 11,000 patient images, VisiRad XR detected lung nodules and masses at a sensitivity of 83% with a fixed false positives per image rate and fixed device operating threshold. For an average hospital in the US performing 50,000 chest X-rays annually, VisiRad XR could identify up to an additional 750 lung nodules or masses in a year.

Clinical evaluation of VisiRad XR was performed through a multi-reader, multi-case clinical validation study using six hundred images and twenty-four radiologists from across the country. VisiRad XR improved each reader’s ability to detect pulmonary nodules and masses within chest X-rays, demonstrated through statistically significant improvement in area under the curve of the receiver operating curve (AUC) and increased sensitivity across all readers, regardless of experience level, specialty, or training background.

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