Artificial intelligence–assisted diagnosis of pediatric supracondylar humerus fractures: a deep learning–based clinical decision support model

Accurate detection of pediatric supracondylar humerus fractures remains a diagnostic challenge in emergency settings. To address this, we developed and validated a transfer-learning model, benchmarking its performance against board-certified emergency physicians. A retrospective dataset of 1440 anonymized pediatric elbow radiographs (720 fractures and 720 normal) was preprocessed using grayscale normalization, aspect-ratio-preserving resizing, adaptive denoising, and contrast-limited adaptive histogram equalization. Data were split at the patient level into training and validation ( n  = 1295) and a held-out test set ( n  = 145). A pretrained ResNet50 backbone was fine-tuned, and model stability was assessed via five-fold stratified cross-validation. Clinical applicability was evaluated in a blinded reader study where 20 emergency physicians interpreted 200 radiographs. On the held-out test set, the model achieved 90.34% accuracy, 93.5% sensitivity, and 86.8% specificity. Five-fold cross-validation yielded a mean accuracy of 91.72%, sensitivity of 92.8%, and specificity of 88.5%. In the reader study, emergency physicians achieved a mean accuracy of 87.6%, sensitivity of 90.5%, and specificity of 84.7%. Diagnostic performance differences between the model and physicians were not statistically significant ( P  > 0.05). The transfer-learning model achieved clinician-comparable performance, indicating its potential utility as an initial triage tool. However, the algorithm shares human diagnostic limitations, exhibiting lower sensitivity for nondisplaced Gartland type I fractures. Therefore, based on current data, it cannot be concluded that artificial intelligence implementation will eliminate the risk of missed fractures. Prospective multicenter validation is required before clinical deployment. Level of Evidence Level III (retrospective diagnostic study).