YOLOv12 Algorithm-Aided Detection and Classification of Lateral Malleolar Avulsion Fracture and Subfibular Ossicle Based on CT Images: Multicenter Study
While MRI (read by senior radiologists) is the gold standard for distinguishing LMAFs from SFOs, CT-based differentiation is challenging for radiologists. A CT-only DCNN (YOLOv12) achieved substantially higher performance than radiologists interpreting CT alone and approached the MRI-based reference standard, highlighting its potential to augment CT-based decision-making where MRI is limited or unavailable.
Authors
- Nan Xu (ORCID: https://orcid.org/0000-0001-9912-2347)
- Xiangsheng Li (ORCID: https://orcid.org/0000-0003-4980-829X)
- Ke Tian (ORCID: https://orcid.org/0009-0001-8660-1952)
- Qian Gao (ORCID: https://orcid.org/0000-0001-8862-9257)
- Jun Zhang (ORCID: https://orcid.org/0000-0002-6666-8703)
- Zihan Chen (ORCID: https://orcid.org/0009-0002-0033-0969)
- Peng Sun (ORCID: https://orcid.org/0000-0002-0663-320X)
- Yousheng Yuan (ORCID: https://orcid.org/0000-0003-3352-9640)
- Liang Xia
- Jiayi Liu
Institutions
- United States Air Force (US)
- Air Force General Hospital PLA (CN)
- Xi’an Jiaotong-Liverpool University (CN)
- Nanjing Medical University (CN)
- Air Force Medical University (CN)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-09-09
- DOI
- https://doi.org/10.2196/79064
- Citations
- 5
- Primary Topic
- Foot and Ankle Surgery
- Type
- article
- Field-Weighted Citation Impact
- 8.61