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

UNLABELLED: 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).