Multinational Validation of a Radiography-Based AI Tool for Appendicular Fracture Detection and Corresponding Reader Performance

Radiology 2026 Open access

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Authors: Huib Ruitenbeek, Mathias W. Brejnebøl, Gözde Güneş, Bas J. van der Zwart, Marna G. Bouwhuis, Jessica D. Lantinga-van den Brink, Dimitar Ivanov Radev, Yahya Fekri Avval, Tazio Maleitzke, Kristine Ifigenia Bunyoz, Frederik Jager Bruun, Katharina Beller
+11 more Haoyin Cao, Luis Alexander Becker, Maximilian Muellner, Anders Lenskjold, Katharina Ziegeler, Dominik Deppe, Daniël Bos, Mikael Boesen, Kay-Geert A. Hermann, Edwin H. G. Oei, Jacob J. Visser
DOI: 10.1148/radiol.243548
Cited by: 1
References: 23

Abstract

In this multinational and multireader study, a radiography-based artificial intelligence tool for fracture detection showed robust performance and conferred the greatest benefits to less experienced readers.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Radiology practices and education Medical Imaging and Analysis

Publication details

Journal / source
Radiology
Publisher
Radiological Society of North America (RSNA)
Volume
320
Issue
2
Pages
e243548–e243548
Publication date
1 August 2026
Language
EN