Purpose <p>Distal radius fractures are among the most common upper extremity injuries. While convolutional neural networks (CNNs) have shown promise in fracture detection, no models have specifically addressed the need for reduction, which is a critical distinction for clinical decision-making. Current AI models detect fractures but fall short in guiding treatment, highlighting an opportunity to enhance triage through AI.</p> Methods <p>We retrospectively assembled 495 patients with AP, lateral, and oblique wrist radiographs. To mirror real-world deployment, images were smartphone-style screenshots standardized to 224 × 224 grayscale. We trained BoraeNet, a three-branch DenseNet-169 model (one branch per view) using 5-fold CV on 90% of patients and evaluated a held-out internal test set (<i>n</i> = 50).</p> Results <p>BoraeNet demonstrated strong performance in classifying fractures needing reduction. Training metrics included accuracy (0.93), precision (0.95), recall (0.91), and F1 score (0.93), with consistent performance across folds. On the held-out internal validation set (<i>n</i> = 50), the model achieved an AUC of 0.89 and average precision of 0.91, classifying 15 true positives, 27 true negatives, 6 false positives, and 2 false negatives.</p> Conclusion <p>The BoraeNet model offers a promising tool using low-fidelity images to predict need for reduction with strong discrimination, supporting triage in resource-limited settings. Future work should include larger datasets with demographic information, object detection, and multi-center external validation and integration into PACS/clinical workflow.</p> <p><i>Level of Evidence</i> Level III.</p>