External validation of a commercially available deep learning algorithm for fracture detection in children

Numerous studies highlight AI's ability to detect minute fractures that may go unnoticed. For instance, a study presented at RSNA 2023 found that AI was more effective than radiologists at detecting commonly missed fractures, suggesting that a radiologist associated with AI approach could improve detection¹. National standards are also beginning to acknowledge AI's role in diagnostic workflows. AI has shown utility in urgent care settings for fracture detection, as evidenced by the UK's National Institute for Health and Care Excellence (NICE). NICE endorsed Rayvolve®, which encompasses four verticals: AZtrauma, AZchest, AZmeasure, and AZboneage, for its ability to reduce diagnostic errors (nice.org.uk)².

AI in radiology holds several benefits for radiologists and hospitals in general. Learn all that AI offers for radiology in this blog and how you can optimize X-ray diagnostics in your healthcare center.

Rayvolve has been recognized by the National Institute for Health and Care Excellence (NICE) as a key AI technology to help healthcare professionals diagnose bone fractures in X-rays across the NHS. The AI solution encompasses four verticals: AZtrauma, AZchest, AZmeasure, and AZboneage. Among these, AZtrauma specifically focuses on detecting fractures, dislocations, and joint effusions. Clinically validated, the solution has demonstrated its ability to tackle one of the biggest challenges in medical imaging—ensuring fractures are diagnosed quickly and accurately, especially during time-pressured workloads in busy clinical environments.