Artificial intelligence for detecting subtle paediatric tibial fractures in children under three years: An analysis of YOLO architectures with convolutional Block attention.
Authors
Affiliations (4)
Affiliations (4)
- Department of Computer Science, Streatham Campus, University of Exeter, North Park Road, Exeter, EX4 4QF, UK.
- Royal Cornwall Hospitals NHS Trust, Treliske, Truro, TR1 3LJ, UK.
- Department of Health and Care Professions, South Cloisters, University of Exeter, St Luke's Campus, Heavitree Road, Exeter, EX1 2LU, UK.
- Department of Computer Science, Streatham Campus, University of Exeter, North Park Road, Exeter, EX4 4QF, UK. Electronic address: [email protected].
Abstract
Subtle tibial fractures in children under three are frequently missed, particularly in cases of suspected physical abuse. Early, accurate identification is essential for clinical decision-making and safeguarding. This study assessed whether YOLO-based object detection models can reliably detect subtle tibial fractures on paediatric radiographs. A retrospective dataset of 245 tibial fractures and 637 non-fracture anterior-posterior radiographs was collected. Fractures were annotated using radiologist-verified polygonal labels. YOLOv5, YOLOv8, and YOLOv11 were trained with five-fold cross-validation, and the effect of adding Convolutional Block Attention Module (CBAM) was examined. Performance was evaluated using mAP50, mAP50-95, and per-image diagnostic accuracy. Eigen-CAM heatmaps were used to assess model interpretability. YOLOv8 achieved the strongest overall performance. Adding CBAM improved localisation and increased mean detection confidence by 10.6%. Buckle fractures were most reliably identified, while periosteal and spiral fractures remained challenging, likely due to limited representation and subtle radiographic appearance. YOLO-based models show potential for detecting subtle tibial fractures in infants and toddlers, and CBAM further enhances performance in difficult cases. Larger multicentre datasets are needed to strengthen generalisability. AI-assisted detection could support radiologists in suspected non-accidental injury, reduce missed fractures, and improve diagnostic pathways, particularly in settings without specialist paediatric radiology expertise.