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

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 (mAP@0.5) of 90.6% and an AUC of 0.921 on the internal test set, and a mAP@0.5 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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