This paper is in the following e-collection/theme issue:

Computer-Aided Diagnosis (240) Artificial Intelligence (2052) Imaging Informatics (294) Medical imaging (104) Tools, Programs and Algorithms (368) Clinical Information and Decision Making (1866) Ontologies, Classifications, and Coding (318)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/79064, first published .
YOLOv12 Algorithm-Aided Detection and Classification of Lateral Malleolar Avulsion Fracture and Subfibular Ossicle Based on CT Images: Multicenter Study

YOLOv12 Algorithm-Aided Detection and Classification of Lateral Malleolar Avulsion Fracture and Subfibular Ossicle Based on CT Images: Multicenter Study

YOLOv12 Algorithm-Aided Detection and Classification of Lateral Malleolar Avulsion Fracture and Subfibular Ossicle Based on CT Images: Multicenter Study

Authors of this article:

Jiayi Liu1 Author Orcid Image ;   Peng Sun2 Author Orcid Image ;   Yousheng Yuan1 Author Orcid Image ;   Zihan Chen3 Author Orcid Image ;   Ke Tian2 Author Orcid Image ;   Qian Gao2 Author Orcid Image ;   Xiangsheng Li2 Author Orcid Image ;   Liang Xia1 Author Orcid Image ;   Jun Zhang1 Author Orcid Image ;   Nan Xu2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Jiayi Liu   1 * , PhD ;   Peng Sun   2 * , MS ;   Yousheng Yuan   1 * , MS ;   Zihan Chen   3 * , BSc ;   Ke Tian   2 , MS ;   Qian Gao   2 , MS ;   Xiangsheng Li   2 , PhD ;   Liang Xia   1 , PhD ;   Jun Zhang   1 , PhD ;   Nan Xu   2 , PhD

1 Department of Radiology, Sir Run Run Hospital, Nanjing Medical University, Nanjing, China

2 Department of Radiology, Air Force Medical Center, Air Force Medical University, Beijing, China

3 School of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China

*these authors contributed equally

Corresponding Author:

  • Nan Xu, PhD
  • Department of Radiology, Air Force Medical Center, Air Force Medical University
  • Fucheng Road 30, Haidian District
  • Beijing 100142
  • China
  • Phone: 86 18510230072
  • Email: xunan835@126.com