AI Assistance Reduces Missed Fractures on Musculoskeletal Radiographs: A Multicentre Multi-Reader Crossover Study

Varování

Publikace nespadá pod Filozofickou fakultu, ale pod Lékařskou fakultu. Oficiální stránka publikace je na webu muni.cz.

Autoři	KLIČNÍK Simon NĚMEC Karel DAVIDOVÁ Marika BILANIN Samuel JIRKŮ Veronika KUBOV Šimon KORBEL Lukáš NOHEJL David OVESNÁ Petra KVAKOVÁ Karolína DANDÁR Jakub KVAK Daniel
Rok publikování	2026
Druh	Recenzovaný odborný článek
Časopis / Zdroj	European Journal of Radiology Artificial Intelligence
Fakulta / Pracoviště MU	Lékařská fakulta
Citace
Doi	https://doi.org/10.1016/j.ejrai.2026.100106
Popis	Purpose: To quantify the impact of AI software on fracture detection on musculoskeletal radiographs in a multicentre multi-reader crossover study. Methods: In this retrospective multicentre study, 726 radiographs from five Czech hospitals were interpreted twice by the seven clinicians: first without AI and, after a 30-day washout, with AI decision support. The endpoint was binary fracture detection. The reference standard was defined by independent review by one trauma surgeon and one radiologist; cases with discordant expert assessments were excluded. Paired analyses were performed on the expert-agreement set. Sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated per reader, and overall * Corresponding author Email address: daniel.kvak@carebot.com (Daniel Kvak) performance was summarised using a nonparametric case-level bootstrap. Results: The primary analysis set included 630 radiographs, of which 197 were fracture-positive and 433 were fracture-negative. Overall bootstrap-estimated sensitivity was 86.26% (80.82;90.41) without AI and 94.56% (90.54; 97.00) with AI (p < 0.001). Overall specificity was 93.79% (91.14;95.72) without AI and 90.21% (87.08;92.69) with AI (p = 0.035). Overall NLR improved from 0.15 (0.10;0.21) to 0.06 (0.03;0.11) (p = 0.008), while overall PLR decreased from 14.24 (9.94;20.84) to 9.75 (7.36;12.97) (p = 0.134). Sensitivity increased in six of seven readers, and the largest gain occurred in the reader with the lowest baseline sensitivity. Conclusion: AI assistance reduced missed fractures across readers, with a modest and reader-dependent increase in false-positive fracture calls. These findings suggest that AI-assisted interpretation may function as a useful second-reader aid in musculoskeletal radiography.