Accuracy of wrist fracture detection on radiographs by artificial intelligence compared to human clinicians. A systematic review and meta-analysis

Abstract

PurposeThe aim of the study is to perform a systematic review and meta-analysis comparing the diagnostic performance of artificial intelligence (AI) and human readers in the detection of wrist fractures. MethodThis study conducted a systematic review following PRISMA guidelines. Medline and Embase databases were searched for relevant articles published up to August 14, 2023. All included studies reported the diagnostic performance of AI to detect wrist fractures, with or without comparison to human readers. A meta-analysis was performed to calculate the pooled sensitivity and specificity of AI and human experts in detecting distal radius, and scaphoid fractures respectively. ResultsOf 213 identified records, 20 studies were included after abstract screening and full-text review. Nine articles examined distal radius fractures, while eight studies examined scaphoid fractures. One study included distal radius and scaphoid fractures, and two studies examined paediatric distal radius fractures. The pooled sensitivity and specificity for AI in detecting distal radius fractures were 0.92 (95% CI 0.88–0.95) and 0.89 (0.84–0.92), respectively. The corresponding values for human readers were 0.95 (0.91–0.97) and 0.94 (0.91–0.96). For scaphoid fractures, pooled sensitivity and specificity for AI were 0.85 (0.73–0.92) and 0.83 (0.76–0.89), while human experts exhibited 0.71 (0.66–0.76) and 0.93 (0.90–0.95), respectively. ConclusionThe results indicate comparable diagnostic accuracy between AI and human readers, especially for distal radius fractures. For the detection of scaphoid fractures, the human readers were similarly sensitive but more specific. These findings underscore the potential of AI to enhance fracture detection accuracy and improve clinical workflow, rather than to replace human intelligence.