DataSheet1_Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty.pdf

Previous studies have shown that the manufacturer’s default preoperative plans for total knee arthroplasty with patient-specific guides require frequent, time-consuming changes by the surgeon. Currently, no research has been done on predicting preoperative plans for orthopedic surgery using machine learning. Therefore, this study aims to evaluate whether artificial intelligence (AI) driven planning tools can create surgeon and patient-specific preoperative plans that require fewer changes by the surgeon. A dataset of 5409 preoperative plans, including the manufacturer’s default and the plans corrected by 39 surgeons, was collected. Features were extracted from the preoperative plans that describe the implant sizes, position, and orientation in a surgeon- and patient-specific manner. Based on these features, non-linear regression models were employed to predict the surgeon’s corrected preoperative plan. The average number of corrections a surgeon has to make to the preoperative plan generated using AI was reduced by 39.7% compared to the manufacturer’s default plan. The femoral and tibial implant size in the manufacturer’s plan was correct in 68.4% and 73.1% of the cases, respectively, while the AI-based plan was correct in 82.2% and 85.0% of the cases, respectively, compared to the surgeon approved plan. Our method successfully demonstrated the use of machine learning to create preoperative plans in a surgeon- and patient-specific manner for total knee arthroplasty.

既往研究显示，针对全膝关节置换术（total knee arthroplasty）的患者专用导板（patient-specific guides），其制造商默认术前计划常需术者进行频繁且耗时的调整。目前尚无利用机器学习（machine learning）预测骨科手术术前计划的相关研究。因此，本研究旨在评估人工智能（artificial intelligence，AI）驱动的规划工具能否生成兼顾术者与患者特异性的术前计划，从而减少术者的调整工作量。本研究收集了共计5409份术前计划数据集，涵盖制造商默认计划与39位术者修正后的计划。从上述术前计划中提取特征，以术者与患者特异性的方式描述植入物的尺寸、位置及朝向。基于上述特征，本研究采用非线性回归模型（non-linear regression models）对术者修正后的术前计划进行预测。与制造商默认计划相比，使用AI生成的术前计划所需的术者平均调整次数降低了39.7%。相较于术者认可的最终计划，制造商默认计划中股骨植入物（femoral implant）与胫骨植入物（tibial implant）尺寸的准确率分别为68.4%与73.1%；而基于AI的计划准确率则分别达到82.2%与85.0%。本研究方法成功验证了利用机器学习可为全膝关节置换术生成兼具术者与患者特异性的术前计划。