Improving Recognition Performance and Reader Efficiency for Femoral-Neck Fracture Detection on Pelvic or Hip Radiographs Using Artificial Intelligence

Radiology 2026

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Authors: Xin-Xiao Lin, Yurui Qian, Shi-Hao Zhou, Shen Han-bin, Yulong Zhang, Jia-Wei Huang, Yuhan Chen, Nai-Feng Tian
DOI: 10.1148/radiol.253931
Cited by: 1
References: 24

Abstract

< .001), respectively. Conclusion OccuNet demonstrated excellent performance for femoral-neck fracture detection on pelvic or hip radiographs, maintained high sensitivity in radiograph-negative or indeterminate (Garden I-II) fractures, and improved reader sensitivity and efficiency.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Hip and Femur Fractures Medical Imaging and Analysis

Publication details

Journal / source
Radiology
Publisher
Radiological Society of North America (RSNA)
Volume
320
Issue
3
Pages
e253931–e253931
Publication date
1 September 2026
Language
EN