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
Key results:
- The AI tool demonstrated consistent performance across three European centers, with AUC values of 0.90, 0.91, and 0.95, showing robust generalizability.
- Subgroup analysis revealed significant underperformance in wrist/hand/finger fracture detection in one center, highlighting the need for local validation and possibly adjustments in anatomy-specific regions.
- The AI tool shows potential as a triage tool to streamline radiology workflows, especially in high-demand environments like emergency departments
Summary statement: The AI tool showed robust and generalizable performance across multiple centers, but anatomy-specific adjustments are needed to optimize accuracy in clinical practice.