Dual-Stream Attention-Based Classification Network for Tibial Plateau Fractures via Diffusion Model Augmentation and Segmentation Map Integration  

作　　者：Yi Xie Zhi-wei Hao Xin-meng Wang Hong-lin Wang Jia-ming Yang Hong Zhou Xu-dong Wang Jia-yao Zhang Hui-wen Yang Peng-ran Liu Zhe-wei Ye 

机构地区：[1]Department of Orthopedics Surgery,Union Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan,430022,China [2]Laboratory of Intelligent Medicine Research,Union Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan,430022,China [3]School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan,430074,China [4]Key Laboratory of Clinical Biochemistry Testing in Universities of Yunnan Province,School of Basic Medical Sciences,Dali University,Dali,671003,China [5]Department of Orthopedics Surgery,Fujian Provincial Hospital,Fuzhou,350001,China [6]Department of Otorhinolaryngology,Union Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan,430022,China

出　　处：《Current Medical Science》2025年第1期57-69,共13页当代医学科学(英文)

基　　金：supported by the National Natural Science Foundation of China(Nos.81974355 and 82172524);Key Research and Development Program of Hubei Province(No.2021BEA161);National Innovation Platform Development Program(No.2020021105012440);Open Project Funding of the Hubei Key Laboratory of Big Data Intelligent Analysis and Application,Hubei University(No.2024BDIAA03);Free Innovation Preliminary Research Fund of Wuhan Union Hospital(No.2024XHYN047).

摘　　要：Objective This study aimed to explore a novel method that integrates the segmentation guidance classification and the dif-fusion model augmentation to realize the automatic classification for tibial plateau fractures(TPFs).Methods YOLOv8n-cls was used to construct a baseline model on the data of 3781 patients from the Orthopedic Trauma Center of Wuhan Union Hospital.Additionally,a segmentation-guided classification approach was proposed.To enhance the dataset,a diffusion model was further demonstrated for data augmentation.Results The novel method that integrated the segmentation-guided classification and diffusion model augmentation sig-nificantly improved the accuracy and robustness of fracture classification.The average accuracy of classification for TPFs rose from 0.844 to 0.896.The comprehensive performance of the dual-stream model was also significantly enhanced after many rounds of training,with both the macro-area under the curve(AUC)and the micro-AUC increasing from 0.94 to 0.97.By utilizing diffusion model augmentation and segmentation map integration,the model demonstrated superior efficacy in identifying SchatzkerⅠ,achieving an accuracy of 0.880.It yielded an accuracy of 0.898 for SchatzkerⅡandⅢand 0.913 for SchatzkerⅣ;for SchatzkerⅤandⅥ,the accuracy was 0.887;and for intercondylar ridge fracture,the accuracy was 0.923.Conclusion The dual-stream attention-based classification network,which has been verified by many experiments,exhibited great potential in predicting the classification of TPFs.This method facilitates automatic TPF assessment and may assist surgeons in the rapid formulation of surgical plans.

关 键 词：Artificial intelligence YOLOv8 Tibial plateau fracture Diffusion model augmentation Segmentation map 

分 类 号：R683.42[医药卫生—骨科学]