Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Med.

Sec. Precision Medicine

Volume 12 - 2025 | doi: 10.3389/fmed.2025.1635016

A Multi-Module Enhanced YOLOv8 Framework for Accurate AO Classification of Distal Radius Fractures: SCFAST-YOLO

Provisionally accepted
  • 1The First Affiliated Hospital of Soochow University, Suzhou, China
  • 2Nantong University, Nantong, China
  • 3Department of Orthopaedics, Affiliated Nantong Hospital 3 of Nantong University, Nantong, China

The final, formatted version of the article will be published soon.

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 f ür Osteosynthesefragen) typing(an internationally recognised 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)) (Jiang et al., 2025) 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. 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. 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.

Keywords: Distal radius fractures, YOLOv8, C2f-Faster-EMA, TADDH, FDPN

Received: 25 May 2025; Accepted: 04 Aug 2025.

Copyright: © 2025 Wang, Sun, Jiang, Shi, Wang, Yang and Qiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Hongwei Yang, Department of Orthopaedics, Affiliated Nantong Hospital 3 of Nantong University, Nantong, China
Yusen Qiao, The First Affiliated Hospital of Soochow University, Suzhou, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.