利用深度学习通过患者性别和体重指数对无标记X线片的膝关节假体进行预测。
Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning.
作者信息
Yue Yu, Gao Qiaochu, Zhao Minwei, Li Dou, Tian Hua
机构信息
Department of Electronics, Peking University, Beijing, China.
Department of Orthopedics, Peking University Third Hospital, and Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, China.
出版信息
Front Surg. 2022 Mar 14;9:798761. doi: 10.3389/fsurg.2022.798761. eCollection 2022.
BACKGROUND
Total knee arthroplasty (TKA) is effective for severe osteoarthritis and other related diseases. Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements, which are inconvenient and inefficient.
METHODS
In this article, we utilize convolutional neural networks to analyze a multimodal patient data and design a system that helps doctors choose prostheses for TKA. To alleviate the problems of insufficient data and uneven distribution of labels, research on model structure, loss function and transfer learning is carried out. Algorithm optimization based on error correct output coding (ECOC) is implemented to further boost the performance.
RESULTS
The experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.
CONCLUSIONS
The results verify that the ECOC-based model for prosthesis prediction in TKA is feasible and outperforms existing methods, which is of great significance for templating.
背景
全膝关节置换术（TKA）对严重骨关节炎及其他相关疾病有效。准确的假体预测是改善TKA术后临床结果和患者满意度的关键因素。当前研究主要集中在传统的手动模板测量上，这种方法不方便且效率低下。
方法
在本文中，我们利用卷积神经网络分析多模态患者数据，并设计了一个帮助医生为TKA选择假体的系统。为缓解数据不足和标签分布不均的问题，我们对模型结构、损失函数和迁移学习进行了研究。基于纠错输出编码（ECOC）实施算法优化以进一步提升性能。
结果
实验结果表明，基于ECOC的模型对股骨组件和胫骨组件的预测准确率分别达到88.23%和86.27%。
结论
结果验证了基于ECOC的TKA假体预测模型是可行的，且优于现有方法，这对模板制作具有重要意义。
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