Duretti Teferi Fufa

This feasibility study trained a convolutional neural network on pre-reduction injury radiographs and combined its outputs with clinical and demographic data in a random forest model to predict whether a group of fellowship-trained hand surgeons at one institution would recommend operative intervention for distal radial fractures in 1,040 patients (884 training, 156 testing); on the test set the combined model achieved 87.14% accuracy, 97% sensitivity, 73% specificity, an area under the ROC curve of 0.96, and a Brier score of 0.10, with Grad-CAM indicating the CNN focused on clinically relevant features such as fracture displacement and SHAP highlighting age and lateral wrist radiographs as key contributors.

This feasibility study trained a convolutional neural network on pre-reduction injury radiographs and combined its outputs with clinical and demographic data in a random forest model to predict whether a group of fellowship-trained hand surgeons at one institution would recommend operative intervention for distal radial fractures in 1,040 patients (884 training, 156 testing); on the test set the combined model achieved 87.14% accuracy, 97% sensitivity, 73% specificity, an area under the ROC curve of 0.96, and a Brier score of 0.10, with Grad-CAM indicating the CNN focused on clinically relevant features such as fracture displacement and SHAP highlighting age and lateral wrist radiographs as key contributors.