Register Now: Image-Based Risk: The Next Frontier in Breast Cancer Screening
News|Articles|December 7, 2023

FDA-Cleared AI Triage Software for Chest X-Rays Offers Enhanced Detection of Pleural Effusion and Pneumothorax

Author(s)Jeff Hall

BraveCX, an artificial intelligence (AI)-enabled software, reportedly has an area under the curve (AUC) of 98 percent for detecting pneumothorax on chest X-rays.

The Food and Drug Administration (FDA) has granted 510(k) clearance for BraveCX, an emerging triage and notification software for adult chest X-rays, that may facilitate timely diagnosis for urgent cases in an emergency room setting.

BraveCX provides specificity rates ranging between 95 to 97 percent, and areas under the curve (AUC) of 96 percent for pleural effusion and 98 percent for pneumothorax, according to Bering, the developer of BraveCX.

Bering pointed out that findings from a million chest X-rays were utilized to develop the artificial intelligence (AI)-powered software, which was subsequently fine-tuned with over 50,000 labeled chest radiographs from radiologists.

"After over three years of research and collaboration with clinical teams, it's so exciting to see BraveCX emerge as a state-of-the-art tool that has actually 'listened to the end user,’” noted Ignat Drozdov, the CEO and founder of Bering. “FDA clearance means BraveCX prioritizes patient safety (while) still delivering the most advanced risk stratification algorithms where they are needed the most."


Related to this article

Key Sessions of Interest for Radiologists at the Upcoming HLTH USA Conference
How will generative AI software be evaluated by the FDA in the future? What’s next for point-of-care ultrasound? What steps have been successful in facilitating adoption of AI applications across enterprise systems? Leaders in radiology and health care will be discussing these topics and more at the upcoming HLTH USA conference.
Current Perspectives on Integrating AI into Radiology
In a recent interview with Diagnostic Imaging, Joseph Cavallo, MD, MBA, offered insights on post-deployment monitoring with AI, the potential for improved workflow efficiency and the ongoing challenge of balancing essential clinical skills and AI literacy in the training of radiology residents and fellows.