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Dual-Approach AI for Pediatric Supracondylar Fractures: Multiclass Radiograph Classification with Explainable AI and
Che-Wei Lin1, Febryan Setiawan2, Syaifullah Hikmahtiyar3
1Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan, Taiwan (C.-W.L, F.S.); Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan (C.-W.L); Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan, Taiwan (C.-W.L); Institute of Medical Informatics, College of Electrical Engineering and Computer Science, National Cheng Kung University, Tainan, Taiwan (C.-W.L); Department of Biotechnology and Bioindustry Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan, Taiwan (C.-W.L).
Rationale And Objectives:
Pediatric supracondylar fractures (SCFs) are the most common elbow injury in children, yet radiographic diagnosis remains challenging due to complex developmental anatomy, with initially missed fracture rates of 17-77%. Prior artificial intelligence (AI) studies have been limited to binary classification frameworks without Gartland subtype differentiation, and no diagnostic test accuracy meta-analysis specific to supracondylar fractures exists. This study aimed to develop the first multiclass deep learning model for SCF detection and Gartland classification and to systematically compare diagnostic performance across AI-based computational diagnostic approaches, including deep learning architectures and radiomics-based machine learning, through meta-analysis.
Materials And Methods:
A retrospective cohort of 1082 pediatric elbow radiographs (2004-2018) was analyzed using YOLOv11 Nano for multiclass classification (No-SCF, Gartland Type I-III; class distribution: No-SCF n=576, Type I n=21, Type II n=125, Type III n=360). Data augmentation mitigated class imbalance, though the limited Type I sample may constrain generalizability for non-displaced fractures. Three patient-level validation strategies were employed: random split (80:10:10), stratified 5-fold cross-validation, and leave-one-out cross-validation. NormEnsemble-HiResCAM explainable AI (XAI) provided visualization of decision-making patterns. A Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA)-compliant meta-analysis synthesized data from four studies (2232 images) encompassing convolutional neural network (CNN)-based and radiomics-based approaches using random-effects modeling.
Results:
Multiclass accuracy without bone segmentation was 90.98%, 93.12%, and 90.81% across random split, stratified k-fold, and leave-one-out cross-validation, respectively; binary accuracy was 88.89%, 92.83%, and 88.46%. Bone segmentation integration consistently improved accuracy across all strategies (multiclass +3.9 throughout; binary +6.2 to +10.2% points). Meta-analysis revealed pooled sensitivity of 92.0% (95% confidence interval [CI]: 87.0-96.0%) and specificity of 85.0% (95% CI: 77.0-92.0%; I²=92.9%). CNN-based algorithms showed numerically higher specificity than the single included radiomics study, though this comparison warrants caution. YOLOv11 achieved the highest specificity (98.0%) and diagnostic odds ratio (365.00). XAI visualizations demonstrated anatomically stratified attention across fracture grades: physeal-centered activation for normal/Type I, expanded physeal activation for Type II, and fragment-boundary foci for Type III.
Conclusion:
YOLOv11 Nano provided the first multiclass deep learning framework for pediatric SCF Gartland subtype classification with strong diagnostic performance. Meta-analysis demonstrated moderate diagnostic performance across included studies, though findings remain preliminary given the limited evidence base and substantial heterogeneity. Prospective multicenter validation is required before clinical deployment.