Artificial intelligence improves resident detection of pediatric and young adult upper extremity fractures.

Purpose: We wished to evaluate if an open-source artificial intelligence (AI) algorithm (https://www.childfx.com) could improve performance of (1) subspecialized musculoskeletal radiologists, (2) radiology residents, and (3) pediatric residents in detecting pediatric and young adult upper extremity fractures. Materials and methods: A set of evaluation radiographs drawn from throughout the upper extremity (elbow, hand/finger, humerus/shoulder/clavicle, wrist/forearm, and clavicle) from 240 unique patients at a single hospital was constructed (mean age 11.3 years, range 0–22 years, 37.9% female). Two fellowship-trained musculoskeletal radiologists, three radiology residents, and two pediatric residents were recruited as readers. Each reader interpreted each case initially without and then subsequently 3–4 weeks later with AI assistance and recorded if/where fracture was present. Results: Access to AI significantly improved area under the receiver operator curve (AUC) of radiology residents (0.768 [0.730–0.806] without AI to 0.876 [0.845–0.908] with AI, P < 0.001) and pediatric residents (0.706 [0.659–0.753] without AI to 0.844 [0.805–0.883] with AI, P < 0.001) in identifying fracture, respectively. There was no evidence of improvement for subspecialized musculoskeletal radiology attendings in identifying fracture (AUC 0.867 [0.832–0.902] to 0.890 [0.856–0.924], P = 0.093). There was no evidence of difference between overall resident AUC with AI and subspecialist AUC without AI (resident with AI 0.863, attending without AI AUC 0.867, P = 0.856). Overall physician radiograph interpretation time was significantly lower with AI (38.9 s with AI vs. 52.1 s without AI, P = 0.030). Conclusion: An openly accessible AI model significantly improved radiology and pediatric resident accuracy in detecting pediatric upper extremity fractures. [ABSTRACT FROM AUTHOR]