Enhanced YOLOv8-seg-SPDConv for accurate Schatzker classification of tibial plateau fractures.

Background: Tibial plateau fractures are typically caused by direct or indirect violent forces, often resulting in collapse fractures of the medial or lateral tibial plateau. Accurate diagnosis and classification of tibial plateau fractures are of significant importance for determining appropriate treatment plans. However, manual Schatzker classification is subject to considerable inter-observer variability, particularly among junior physicians and non-specialist clinicians, often resulting in suboptimal accuracy and prolonged decision-making time in high-throughput emergency settings. Objective: This study aimed to develop and validate a lightweight YOLOv8n-seg-SPDConv model for the joint instance segmentation and Schatzker classification of tibial plateau fractures using thin-slice axial CT images from 552 unique patients. Specifically, by integrating the Space-to-Depth Convolution (SPDConv) module, we sought to preserve sub-2 mm cortical disruptions while reducing model complexity and suggesting potential for future deployment on resource-constrained platforms. Using a hold-out test set benchmarked against a senior orthopedic surgeon's consensus standard, we systematically evaluated the model's diagnostic performance, inference efficiency (226 FPS), and interpretability via Gradient-weighted Class Activation Mapping (Grad-CAM), with the ultimate goal of serving as a reliable real-time clinical adjunct that supports, rather than supplants, clinicians in emergency and perioperative decision-making. Methods: From October 2017 to January 2026, a dataset comprising 552 axial CT slices from unique patients was collected. Each image was independently annotated into six clinically relevant categories according to the Schatzker classification. To prevent data leakage, dataset partitioning was strictly performed prior to augmentation using an 8:2 patient-wise split. The YOLOv8n-seg network was employed for joint segmentation and classification, with multiple preprocessing techniques applied to ensure data consistency and enhance model performance. Results: The baseline YOLOv8n-seg achieved mAP50 values of 0.941, 0.956, 0.861, 0.818, 0.916, and 0.869 for Schatzker types I-VI, respectively. The improved YOLOv8n-seg-SPDConv yielded mAP50 of 0.928, 0.952, 0.929, 0.859, 0.925, and 0.893 for the six subtypes, with an overall mAP50 increasing from 0.893 to 0.914. Notably, the proposed model reduced parameters from 3.01 M to 2.64 M and FLOPs from 8.1 G to 7.3 G, while increasing inference speed from 188 to 226 FPS, meeting real-time requirements for edge deployment. Grad-CAM visualization confirmed that the model focused its attention on clinically meaningful regions, including fracture fissures and articular depressions, rather than background artifacts. Discussion: This deep learning-based classification method provides efficient and reliable automated assessments to assist and augment manual evaluation by clinicians, with the potential to help reduce interobserver variability among junior physicians during emergency and perioperative decision-making. It shows promise as a tool that may help mitigate interobserver variability and support junior physicians in emergency and primary care settings. While marginal performance variations were observed in simple linear fractures, the model achieved substantial gains in complex, low-contrast subtypes. These findings highlight its value as a complementary clinical decision support tool, with the potential to support and streamline diagnostic workflows rather than replace expert judgment, ultimately contributing to improved patient outcomes.