Deep learning for pediatric femoral neck fracture detection in a multicenter study

Pediatric femoral neck fractures (FNFs) are uncommon but may result in severe complications if undiagnosed. This study developed a deep learning model for automated detection and localization of FNFs on pediatric hip radiographs. The model was trained on 2,594 hip radiographs from 2,116 patients across eight centers. The optimal model (YOLOv11s) achieved a mean average precision at 0.5 IoU threshold ([email protected]) of 90.6% and an AUC of 0.921 on the internal test set, and a [email protected] of 96.8% and an AUC of 0.968 on the external test set. To our knowledge, this represents one of the most comprehensive multicenter AI diagnostic studies for detecting pediatric FNFs. In a single-center reader study, AI assistance significantly improved diagnostic performance among emergency department orthopedic surgeons, particularly those with limited experience. These findings suggest the potential clinical utility of this model for supporting decision-making in emergency settings.

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Publication Details

Journal
iScience
Published
2025-12-09
DOI
https://doi.org/10.1016/j.isci.2025.114372
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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article

Deep learning for pediatric femoral neck fracture detection in a multicenter study

Wenli Gao, Pengran Liu, Tao Zhang, Ning Ling et al.
iScience
Artificial Intelligence in Healthcare and Education
article

Deep learning for pediatric femoral neck fracture detection in a multicenter study

Wenli Gao, Pengran Liu, Tao Zhang, Ning Ling, Jialang Hu, W.H. Tai, Tongtong Huo, Xudong Wang, Lei Jiang, Xiaoliang Chen, Jiaming Yang, Zhewei Ye, Mingdi Xue, Haocheng Xu, Lek Hang Cheang
article en

Abstract

Pediatric femoral neck fractures (FNFs) are uncommon but may result in severe complications if undiagnosed. This study developed a deep learning model for automated detection and localization of FNFs on pediatric hip radiographs. The model was trained on 2,594 hip radiographs from 2,116 patients across eight centers. The optimal model (YOLOv11s) achieved a mean average precision at 0.5 IoU threshold ([email protected]) of 90.6% and an AUC of 0.921 on the internal test set, and a [email protected] of 96.8% and an AUC of 0.968 on the external test set. To our knowledge, this represents one of the most comprehensive multicenter AI diagnostic studies for detecting pediatric FNFs. In a single-center reader study, AI assistance significantly improved diagnostic performance among emergency department orthopedic surgeons, particularly those with limited experience. These findings suggest the potential clinical utility of this model for supporting decision-making in emergency settings.

iScienceVol. 29(1)
Ningxia Hui Autonomous Region Peoples Hospital (CN), Ningxia Medical University (CN), Wuhan Union Hospital (CN), Ningxia Medical University General Hospital (CN), Union Hospital (CN), The Fourth People's Hospital of Ningxia Hui Autonomous Region (CN), Hospital Conde S. Januário (CN), Wuhan University of Science and Technology (CN), Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 67%
Artificial Intelligence in Healthcare and Education
DOIOpen Access PDFOpenAlex
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