ORIGINAL RESEARCH article
Front. Med.
Sec. Precision Medicine
Improved YOLOv8 with Average Pooling Downsampling for Detection and Classification of Intertrochanteric Femoral Fractures in X-ray Images: A Study Focusing on AO/OTA Classification
Zheming Shen 1
Yu Wang 1
Yu Chen 1
Haowen Lu 1
Can Tang 1
Zhiheng Gao 1
Xuequan Zhao 2
Haifu Sun 1
Yuchen Qian 1
Youbin Zhang 1
Yusen Qiao 1
1. The First Affiliated Hospital of Soochow University, Suzhou, China
2. Cangzhou Integrated Traditional Chinese and Western Medicine Hospital, Cangzhou, China
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Abstract
Objective: This study aims to develop an artificial intelligence system for the accurate detection and classification of intertrochanteric femoral fractures (types A1, A2, and A3 according to the AO/OTA classification) in X-ray images, focusing on improving precision and optimizing computational efficiency. Methods: This study adopted a retrospective design, using 976 X-ray image datasets collected from hospital archives. The images were preprocessed, annotated by orthopedic specialists, and divided into training and test sets. The model was improved by replacing the traditional convolutional downsampling modules in YOLOv8 with Average Pooling Downsampling (ADown) modules to enhance feature extraction for small fracture targets. Model training incorporated data augmentation techniques and was evaluated using metrics such as precision, recall, and mean Average Precision (mAP). Results: The proposed YOLOv8-ADown model achieved an overall mAP50 of 81.7%, higher than the 80.5% of the original YOLOv8. The detection precision for A1, A2, and A3 type fractures increased by 7.3%, 3.5%, and 7.8%, respectively. Furthermore, the number of model parameters was reduced by 12.3%, and computational complexity (FLOPs) was decreased by 9.8%, demonstrating potential for deployment on edge devices. Conclusion: The YOLOv8-ADown model provides an efficient solution for fracture detection and is expected to assist in clinical diagnosis. Future work should address data collection challenges and conduct multi-center validation.
Summary
Keywords
artificial intelligence, Hip fracture, Intertrochanteric femoral fracture, medical imaging, object detection, YOLOv8
Received
02 December 2025
Accepted
09 February 2026
Copyright
© 2026 Shen, Wang, Chen, Lu, Tang, Gao, Zhao, Sun, Qian, Zhang 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: Haifu Sun; Yuchen Qian; Youbin Zhang; Yusen Qiao
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.