A multi-module enhanced YOLOv8 framework for accurate AO classification of distal radius fractures: SCFAST-YOLO.

Introduction: CT-based classification of distal ulnar-radius fractures requires precise detection of subtle features for surgical planning, yet existing methods struggle to balance accuracy with clinical efficiency. This study aims to develop a lightweight architecture that achieves accurate AO (Arbeitsgemeinschaft für Osteosynthesefragen) typing[an internationally recognized fracture classification system based on fracture location, degree of joint surface involvement, and comminution, divided into three major categories: A (extra-articular), B (partially intra-articular), and C (completely intra-articular)] while maintaining real-time performance. In this task, the major challenges are capturing complex fracture morphologies without compromising detection speed and ensuring precise identification of small articular fragments critical for surgical decision-making. Methods: We propose SCFAST-YOLO framework to address these challenges. Its first contribution is introducing the SCConv module, which integrates Spatial and Channel Reconstruction Units to systematically reduce feature redundancy while preserving discriminative information essential for detecting subtle articular fragments. Secondly, we develop the C2f-Faster-EMA module that preserves fine-grained spatial details through optimized information pathways and statistical feature aggregation. Third, our Feature-Driven Pyramid Network facilitates multi-resolution feature fusion across scales for improved detection. Finally, we implement a Target-Aware Dual Detection Head that employs task decomposition to enhance localization precision. Results and discussion: Evaluated on our FHSU-DRF dataset (332 cases, 1,456 CT sequences), SCFAST-YOLO achieves 91.8% mAP@0.5 and 87.2% classification accuracy for AO types, surpassing baseline YOLOv8 by 2.1 and 2.3 percentage points respectively. The most significant improvements appear in complex Type C fractures (3.2 percentage points higher classification accuracy) with consistent average recall of 0.85-0.88 across all fracture patterns. The model maintains real-time inference (52.3 FPS) while reducing parameters, making it clinically viable. Extensive qualitative and quantitative results demonstrate the advantages of our approach. Additionally, we show the broader clinical applications of SCFAST-YOLO in enhancing consistency and efficiency in trauma care.