Hwang Doohyun, Ahn Sungho, Park Yong-Beom, Kim Seong Hwan, Han Hyuk-Soo, Lee Myung Chul, Ro Du Hyun
Department of Orthopedic Surgery, Seoul National University College of Medicine, Seoul 03080, Korea.
Department of Orthopedic Surgery, Seoul National University Hospital, Seoul 03080, Korea.
J Clin Med. 2022 Jun 22;11(13):3612. doi: 10.3390/jcm11133612.
Sarcopenia, an age-related loss of skeletal muscle mass and function, is correlated with adverse outcomes after some surgeries. Here, we present a deep-learning-based model for automatic muscle segmentation and quantification of full-leg plain radiographs. We illustrated the potential of the model to predict sarcopenia in patients undergoing total knee arthroplasty (TKA). A U-Net-based deep learning model for automatic muscle segmentation was developed, trained and validated on the plain radiographs of 227 healthy volunteers. The radiographs of 403 patients scheduled for primary TKA were reviewed to test the developed model and explore its potential to predict sarcopenia. The proposed deep learning model achieved mean IoU values of 0.959 (95% CI 0.959-0.960) and 0.926 (95% CI 0.920-0.931) in the training set and test set, respectively. The fivefold AUC value of the sarcopenia classification model was 0.988 (95% CI 0.986-0.989). Of seven key predictors included in the model, the predicted muscle volume (PMV) was the most important of these features in the decision process. In the preoperative clinical setting, wherein laboratory tests and radiographic imaging are available, the proposed deep-learning-based model can be used to screen for sarcopenia in patients with knee osteoarthritis undergoing TKA with high sarcopenia screening performance.
肌肉减少症是一种与年龄相关的骨骼肌质量和功能丧失，与某些手术后的不良后果相关。在此，我们提出了一种基于深度学习的模型，用于全腿平片的肌肉自动分割和量化。我们展示了该模型在预测接受全膝关节置换术（TKA）患者的肌肉减少症方面的潜力。开发了一种基于U-Net的深度学习模型用于自动肌肉分割，并在227名健康志愿者的平片上进行训练和验证。对403例计划进行初次TKA的患者的X线片进行回顾，以测试所开发的模型并探索其预测肌肉减少症的潜力。所提出的深度学习模型在训练集和测试集中的平均交并比（IoU）值分别为0.959（95%可信区间0.959 - 0.960）和0.926（95%可信区间0.920 - 0.931）。肌肉减少症分类模型的五重曲线下面积（AUC）值为0.988（95%可信区间0.986 - 0.989）。在模型包含的七个关键预测因子中，预测肌肉体积（PMV）在决策过程中是这些特征中最重要的。在术前临床环境中，当实验室检查和影像学检查可用时，所提出的基于深度学习的模型可用于对接受TKA的膝骨关节炎患者进行肌肉减少症筛查，具有较高的肌肉减少症筛查性能。
