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

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.