Kim Man-Soo, Cho Ryu-Kyoung, Yang Sung-Cheol, Hur Jae-Hyeong, In Yong
Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea.
Bioengineering (Basel). 2023 May 23;10(6):632. doi: 10.3390/bioengineering10060632.
(1) Background: The purpose of this study was to investigate whether the loosening of total knee arthroplasty (TKA) implants could be detected accurately on plain radiographs using a deep convolution neural network (CNN). (2) Methods: We analyzed data for 100 patients who underwent revision TKA due to prosthetic loosening at a single institution from 2012 to 2020. We extracted 100 patients who underwent primary TKA without loosening through a propensity score, matching for age, gender, body mass index, operation side, and American Society of Anesthesiologists class. Transfer learning was used to prepare a detection model using a pre-trained Visual Geometry Group (VGG) 19. For transfer learning, two methods were used. First, the fully connected layer was removed, and a new fully connected layer was added to construct a new model. The convolutional layer was frozen without training, and only the fully connected layer was trained (transfer learning model 1). Second, a new model was constructed by adding a fully connected layer and varying the range of freezing for the convolutional layer (transfer learning model 2). (3) Results: The transfer learning model 1 gradually increased in accuracy and ultimately reached 87.5%. After processing through the confusion matrix, the sensitivity was 90% and the specificity was 100%. Transfer learning model 2, which was trained on the convolutional layer, gradually increased in accuracy and ultimately reached 97.5%, which represented a better improvement than for model 1. Processing through the confusion matrix affirmed that the sensitivity was 100% and the specificity was 97.5%. (4) Conclusions: The CNN algorithm, through transfer learning, shows high accuracy for detecting the loosening of TKA implants on plain radiographs.
(1) 背景：本研究的目的是调查使用深度卷积神经网络（CNN）能否在普通X线片上准确检测出全膝关节置换术（TKA）植入物的松动情况。(2) 方法：我们分析了2012年至2020年在单一机构因假体松动而接受翻修TKA的100例患者的数据。我们通过倾向评分法选取了100例未发生松动的初次TKA患者，匹配年龄、性别、体重指数、手术侧别及美国麻醉医师协会分级。使用迁移学习，利用预训练的视觉几何组（VGG）19来制备检测模型。对于迁移学习，采用了两种方法。首先，去除全连接层，并添加新的全连接层以构建新模型。卷积层冻结不训练，仅训练全连接层（迁移学习模型1）。其次，通过添加全连接层并改变卷积层的冻结范围构建新模型（迁移学习模型2）。(3) 结果：迁移学习模型1的准确率逐渐提高，最终达到87.5%。经混淆矩阵处理后，敏感性为90%，特异性为100%。在卷积层上训练的迁移学习模型2的准确率逐渐提高，最终达到97.5%，比模型1有更好的提升。经混淆矩阵处理证实，敏感性为100%，特异性为97.5%。(4) 结论：CNN算法通过迁移学习，在普通X线片上检测TKA植入物松动方面显示出高准确率。
