School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou, China.
School of Graduate Studies, Management and Science University, Shah Alam, 40100, Selangor, Malaysia.
Sci Rep. 2024 Nov 21;14(1):28885. doi: 10.1038/s41598-024-80441-y.
This work is to investigate the diagnostic value of a deep learning-based magnetic resonance imaging (MRI) image segmentation (IS) technique for shoulder joint injuries (SJIs) in swimmers. A novel multi-scale feature fusion network (MSFFN) is developed by optimizing and integrating the AlexNet and U-Net algorithms for the segmentation of MRI images of the shoulder joint. The model is evaluated using metrics such as the Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity (SE). A cohort of 52 swimmers with SJIs from Guangzhou Hospital serve as the subjects for this study, wherein the accuracy of the developed shoulder joint MRI IS model in diagnosing swimmers' SJIs is analyzed. The results reveal that the DSC for segmenting joint bones in MRI images based on the MSFFN algorithm is 92.65%, with PPV of 95.83% and SE of 96.30%. Similarly, the DSC for segmenting humerus bones in MRI images is 92.93%, with PPV of 95.56% and SE of 92.78%. The MRI IS algorithm exhibits an accuracy of 86.54% in diagnosing types of SJIs in swimmers, surpassing the conventional diagnostic accuracy of 71.15%. The consistency between the diagnostic results of complete tear, superior surface tear, inferior surface tear, and intratendinous tear of SJIs in swimmers and arthroscopic diagnostic results yield a Kappa value of 0.785 and an accuracy of 87.89%. These findings underscore the significant diagnostic value and potential of the MRI IS technique based on the MSFFN algorithm in diagnosing SJIs in swimmers.
本研究旨在探讨基于深度学习的磁共振成像（MRI）图像分割（IS）技术在游泳运动员肩关节损伤（SJIs）中的诊断价值。通过优化和整合 AlexNet 和 U-Net 算法，开发了一种新的多尺度特征融合网络（MSFFN），用于分割肩关节 MRI 图像。该模型使用 Dice 相似系数（DSC）、阳性预测值（PPV）和灵敏度（SE）等指标进行评估。研究对象为来自广州医院的 52 名 SJIs 游泳运动员，分析了所开发的肩关节 MRI IS 模型在诊断游泳运动员 SJIs 中的准确性。结果表明，基于 MSFFN 算法分割 MRI 图像关节骨的 DSC 为 92.65%，PPV 为 95.83%，SE 为 96.30%。同样，分割 MRI 图像肱骨的 DSC 为 92.93%，PPV 为 95.56%，SE 为 92.78%。MRI IS 算法对游泳运动员 SJIs 类型的诊断准确率为 86.54%，超过了传统诊断准确率 71.15%。游泳运动员 SJIs 的完全撕裂、上表面撕裂、下表面撕裂和肌腱内撕裂的诊断结果与关节镜诊断结果之间的一致性，Kappa 值为 0.785，准确率为 87.89%。这些发现突显了基于 MSFFN 算法的 MRI IS 技术在诊断游泳运动员 SJIs 中的重要诊断价值和潜力。
