中国组织工程研究2026，Vol.30Issue(30)：7823-7829,7.DOI:10.12307/2026.449
基于CT影像多任务深度学习模型预测股骨头坏死病变分割及塌陷风险
Multi-task deep learning model of CT images for lesion segmentation and collapse risk prediction in osteonecrosis of the femoral head
摘要
Abstract
BACKGROUND:Previous studies have developed radiomics-based machine learning models for the diagnosis and staging of osteonecrosis of the femoral head.However,uncertainty in collapse prediction results may lead to unnecessary early intervention,and the lack of follow-up data from non-surgical patients has limited research on collapse prediction. OBJECTIVE:To develop a machine learning model based on CT images and establish an automated pipeline for predicting the risk of femoral head collapse. METHODS:Using the hip joint as the analysis unit,99 patients with Association Research Circulation Osseous(ARCO)stage Ⅰ and Ⅱ osteonecrosis of the femoral head(175 hips)were included.The data were divided into training and test sets at a ratio of 8:2.The proposed model employed a multi-task learning architecture with a shared encoder to simultaneously perform necrotic region segmentation and collapse risk prediction.Uncertainty modeling was introduced to quantify prediction uncertainty,enhancing model robustness and generalization.Model performance was evaluated using five-fold cross-validation. RESULTS AND CONCLUSION:In the task of femoral head collapse risk prediction,the multi-task model incorporating uncertainty modeling(MTL-Uncer)achieved an area under the curve(AUC)of 0.84±0.04 and an F1 score of 0.76±0.04,significantly outperforming the single-task model(AUC:0.71±0.07,F1:0.66±0.08).In the necrotic region segmentation task,the MTL-Uncer model achieved a Dice coefficient of 0.901±0.003 and an intersection over union of 0.819±0.004,outperforming the single-task model.Decision curve analysis showed that the MTL-Uncer model had significantly better clinical net benefit within the risk threshold range of 0.1-0.8.Overall,this study developed several deep learning models for osteonecrosis of the femoral head based on CT images,among which the MTL-Uncer model demonstrates the best predictive performance and holds promise as an effective tool for predicting femoral head collapse risk.关键词
深度学习/人工智能/股骨头坏死/CT影像/股骨头塌陷/风险预测Key words
deep learning/artificial intelligence/osteonecrosis of the femoral head/CT imaging/femoral head collapse/risk prediction分类
医药卫生引用本文复制引用
陈雨森,关子欣,何亚青,马茂潇,李平诗,周武,陈雷雷..基于CT影像多任务深度学习模型预测股骨头坏死病变分割及塌陷风险[J].中国组织工程研究,2026,30(30):7823-7829,7.基金项目
国家自然科学基金项目(82374478),项目负责人:陈雷雷 （82374478）
广东省中医药管理局项目(20231118),项目负责人:陈雷雷 （20231118）
广东省科技厅项目(2023A1515010551),项目负责人:陈雷雷 National Natural Science Foundation of China,No.82374478(to CLL) （2023A1515010551）
Guangdong Provincial Administration of Traditional Chinese Medicine Project,No.20231118(to CLL) （to CLL）
Department of Science and Technology of Guangdong Province Project,No.2023A1515010551(to CLL) （to CLL）
