Development and Validation of an Artificial Intelligence Preoperative Planning System for Total Hip Arthroplasty

工作流程 手术计划 计算机科学 深度学习 分割 全髋关节置换术 人工智能 医学物理学 外科 医学 数据库
作者
Xi Chen,Xingyu Liu,Yiou Wang,Ruichen Ma,Shibai Zhu,Shanni Li,Songlin Li,Xiying Dong,Hairui Li,Guangzhi Wang,Yaojiong Wu,Shujun Zhang,Gui-Xing Qiu,Wenwei Qian
出处
期刊:Frontiers in Medicine [Frontiers Media]
卷期号:9 被引量:22
标识
DOI:10.3389/fmed.2022.841202
摘要

Background Accurate preoperative planning is essential for successful total hip arthroplasty (THA). However, the requirements of time, manpower, and complex workflow for accurate planning have limited its application. This study aims to develop a comprehensive artificial intelligent preoperative planning system for THA (AIHIP) and validate its accuracy in clinical performance. Methods Over 1.2 million CT images from 3,000 patients were included to develop an artificial intelligence preoperative planning system (AIHIP). Deep learning algorithms were developed to facilitate automatic image segmentation, image correction, recognition of preoperative deformities and postoperative simulations. A prospective study including 120 patients was conducted to validate the accuracy, clinical outcome and radiographic outcome. Results The comprehensive workflow was integrated into the AIHIP software. Deep learning algorithms achieved an optimal Dice similarity coefficient (DSC) of 0.973 and loss of 0.012 at an average time of 1.86 ± 0.12 min for each case, compared with 185.40 ± 21.76 min for the manual workflow. In clinical validation, AIHIP was significantly more accurate than X-ray-based planning in predicting the component size with more high offset stems used. Conclusion The use of AIHIP significantly reduced the time and manpower required to conduct detailed preoperative plans while being more accurate than traditional planning method. It has potential in assisting surgeons, especially beginners facing the fast-growing need for total hip arthroplasty with easy accessibility.
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