A deep learning knowledge distillation framework using knee MRI and arthroscopy data for meniscus tear detection

作者：Mengjie Ying, Yufan Wang, Kai Yang, Haoyuan Wang, Xudong Liu · 发表于：Frontiers in Bioengineering and Biotechnology · 年份：2024 · DOI：10.3389/fbioe.2023.1326706 · 被引用次数：20 · 研究领域：Knee injuries and reconstruction techniques、Orthopedic Surgery and Rehabilitation、Shoulder Injury and Treatment

Purpose: To construct a deep learning knowledge distillation framework exploring the utilization of MRI alone or combing with distilled Arthroscopy information for meniscus tear detection. Methods: A database of 199 paired knee Arthroscopy-MRI exams was used to develop a multimodal teacher network and an MRI-based student network, which used residual neural networks architectures. A knowledge distillation framework comprising the multimodal teacher network T and the monomodal student network S was proposed. We optimized the loss functions of mean squared error (MSE) and cross-entropy (CE) to enable the student network S to learn arthroscopic information from the teacher network T through our deep learning knowledge distillation framework, ultimately resulting in a distilled student network S T . A coronal proton density (PD)-weighted fat-suppressed MRI sequence was used in this study. Fivefold cross-validation was employed, and the accuracy, sensitivity, specificity, F1-score, receiver operating characteristic (ROC) curves and area under the receiver operating characteristic curve (AUC) were used to evaluate the medial and lateral meniscal tears detection performance of the models, including the undistilled student model S , the distilled student model S T and the teacher model T . Results: The AUCs of the undistilled student model S , the distilled student model S T , the teacher model T for medial meniscus (MM) tear detection and lateral meniscus (LM) tear detection are 0.7...