Diagnostic Performance of Arti fi cial Intelligence for Detection of Scaphoid and Distal Radius Fractures: A Systematic Review

Purpose To review the existing literature to (1) determine the diagnostic ef fi cacy of arti fi cial intelligence (AI) models for detecting scaphoid and distal radius fractures and (2) compare the ef fi cacy to human clinical experts. Methods PubMed, OVID/Medline, and Cochrane libraries were queried for studies investigating the development, validation, and analysis of AI for the detection of scaphoid or distal radius fractures. Data regarding study design, AI model development and architecture, prediction accuracy/area under the receiver operator characteristic curve (AUROC), and imaging modalities were recorded. Results A total of 21 studies were identi fi ed, of which 12 (57.1%) used AI to detect fractures of the distal radius, and nine (42.9%) used AI to detect fractures of the scaphoid. AI models demonstrated good diagnostic performance on average, with AUROC values ranging from 0.77 to 0.96 for scaphoid fractures and from 0.90 to 0.99 for distal radius fractures. Accuracy of AI models ranged between 72.0% to 90.3% and 89.0% to 98.0% for scaphoid and distal radius fractures, respectively. When compared to clinical experts, 13 of 14 (92.9%) studies reported that AI models demonstrated comparable or better performance. The type of fracture in fl uenced model performance, with worse overall performance on occult scaphoid fractures; however, models trained speci fi cally on occult fractures demonstrated substantially improved performance when compared to humans. Conclusions AI models demonstrated excellent performance for detecting scaphoid and distal radius fractures, with the majority demonstrating comparable or better performance compared with human experts. Worse performance was demonstrated on occult fractures. However, when trained speci fi cally on dif fi cult fracture patterns, AI models demonstrated improved performance. Clinical relevance AI models can help detect commonly missed occult fractures while enhancing work fl ow ef fi ciency for distal radius and scaphoid fracture diagnoses. As performance varies based on fracture type, future studies focused on wrist fracture detection should clearly de fi ne whether the goal is to (1) identify dif fi cult-to-detect fractures or (2) improve work fl ow ef fi ciency by assisting in routine tasks. (J Hand Surg Am. 2024;49(5):411 e 422. Copyright (c) 2024 by the American Society for Surgery of the Hand. All rights reserved.)