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Congress: ECR25
Poster Number: C-16080
Type: Poster: EPOS Radiologist (scientific)
Authorblock: B. J. Van Der Zwart1, H. C. Ruitenbeek1, F. J. Bruun2, A. Lenskjold2, M. Boesen2, K. Ziegeler3, K-G. A. Hermann3, E. Oei1, J. J. Visser1; 1Rotterdam/NL, 2Copenhagen/DK, 3Berlin/DE
Disclosures:
Bastiaan Johannes Van Der Zwart: Nothing to disclose
Huibert C Ruitenbeek: Nothing to disclose
Frederik J. Bruun: Nothing to disclose
Anders Lenskjold: Nothing to disclose
Mikael Boesen: Advisory Board: Radiobotics
Katharina Ziegeler: Nothing to disclose
Kay-Geert A. Hermann: Nothing to disclose
Edwin Oei: Nothing to disclose
Jacob Johannes Visser: Nothing to disclose
Keywords: Artificial Intelligence, Musculoskeletal bone, Conventional radiography, Comparative studies, Computer Applications-Detection, diagnosis, Trauma
Purpose

Objective: This study aimed to evaluate the performance and generalizability of an AI tool for fracture detection across three European hospitals using consecutive clinical cases to simulate real-world conditions.
GALLERY