Ability of Deep Learning to Predict Surgical Recommendations for Distal Radial Fractures

Abstract

Background: Distal radial fractures (DRFs) are extremely common, and decisions regarding operative intervention rely on clinical judgment and radiographic parameters at and after injury. Understanding whether a recommendation will be made for operative versus nonoperative management in an initial point-of-care setting can assist in patient counseling and reinforce timely follow-up with general orthopaedic or hand specialists. This study investigated the feasibility of using artificial intelligence (AI) to predict whether a fellowship-trained hand surgeon would recommend operative intervention using pre-reduction radiographs and clinical and demographic data. Methods: A convolutional neural network (CNN) model was trained on pre-reduction injury radiographs, and its outputs were combined with clinical and demographic data in a random forest (RF) model. The final model was evaluated on a hold-out test data set. To enhance interpretability, gradient-weighted class activation mapping (Grad-CAM) heatmaps and SHapley Additive exPlanations (SHAP) were employed to identify image regions and clinical features contributing to model predictions. Results: Of 1,040 included patients, 884 were used for training and 156 for testing model performance. On the test data set, the combined model achieved an accuracy of 87.14%, sensitivity of 97%, specificity of 73%, area under the receiver operating characteristic curve of 0.96, and Brier score of 0.10. Grad-CAM visualizations indicated that the CNN focused on clinically relevant features, such as fracture displacement, and SHAP analysis of the RF model highlighted age and lateral wrist radiographs as key contributors to predictions. Conclusions: This pilot study demonstrates the feasibility of using AI to predict the recommendations of a group of fellowship-trained hand surgeons at 1 institution regarding operative versus nonoperative treatment for a DRF on the basis of pre-reduction injury radiographs and clinical and demographic data. Future work will focus on external validation, expanding data sets, and incorporating additional imaging features to optimize performance and generalizability. Level of Evidence: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.