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

Abstract

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.

Keywords: arthroplasty; artificial intelligence; convolutional neural network; hip; preoperative planning.

FIGURE 1

Flow chart of the development and clinical validation of artificial intelligence preoperative planning system for THA (AIHIP).

FIGURE 2

Development of artificial intelligence preoperative planning system for THA (AIHIP): image segmentation. (A) Net-work structure; (B) segmentation of pelvis and femur. Images of original CT, manual segmentation, and automatic segmentation with AIHIP in four primary diseases: avascular necrosis (AVN), femoral neck fracture (FNF), osteoarthritis (OA), and developmental dysplasia of hip (DDH). 3D reconstruction of the CT was completed after segmentation; (C) performance of AIHIP in automatic segmentation. Dice similarity coefficient (DSC) of training set and validation set. Loss of training set and validation set; (D) time comparison between manual segmentation and artificial intelligence (AI) segmentation. Time comparison between manual correction and AI correction. ***p < 0.001.

FIGURE 3

Development of artificial intelligence preoperative planning system for THA (AIHIP): correction of pelvis, identification of anatomical landmarks and recognition of preoperative deformities. (A) Manual correction and measurement of pelvis and femur; (B) network structure used to identify featured anatomic landmarks; (C) examples of automatic identification of anterior superior iliac spine (ASIS), medial point of lesser trochanter and center of femoral head. The anatomic axis of femur was identified using least square method.

FIGURE 4

Preoperative planning using artificial intelligent preoperative planning system for THA (AIHIP). (A) From left to right: 3D reconstructed pelvis and femur; simulated hip X-ray; simulated postoperative outcome; postoperative X-ray; (B) preoperative planning of acetabular component. The green circle shows the planned component position in real-time. Bone coverage was calculated once the size, position, inclination, and anteversion of acetabular component is determined; (C) preoperative planning of femoral component. The red circle shows the planned position of femoral component in real-time.

FIGURE 5

Clinical validation of artificial intelligent preoperative planning system for THA (AIHIP). (A) Plan accuracy of cup size; (B) plan accuracy of stem size; (C) proportion of high offset/varus stem used; (D) postoperative leg length discrepancy (LLD); (E) difference between preoperative and postoperative offset; (F) operation time. *, **, *** P < 0.05, 0.01, 0.001.