Objective: To develop an artificial intelligence based three-dimensional (3D) preoperative planning system (AIHIP) for total hip arthroplasty (THA) and verify its accuracy by preliminary clinical application.
Methods: The CT image database consisting of manually segmented CT image series was built up to train the independently developed deep learning neural network. The deep learning neural network and preoperative planning module were assembled within a visual interactive interface-AIHIP. After that, 60 patients (60 hips) with unilateral primary THA between March 2017 and May 2020 were enrolled and divided into two groups. The AIHIP system was applied in the trial group ( n=30) and the traditional acetate templating was applied in the control group ( n=30). There was no significant difference in age, gender, operative side, and Association Research Circulation Osseous (ARCO) grading between the two groups ( P>0.05). The coincidence rate, preoperative and postoperative leg length discrepancy, the difference of bilateral femoral offsets, the difference of bilateral combined offsets of two groups were compared to evaluate the accuracy and efficiency of the AIHIP system.
Results: The preoperative plan by the AIHIP system was completely realized in 27 patients (90.0%) of the trial group and the acetate templating was completely realized in 17 patients (56.7%) of the control group for the cup, showing significant difference ( P<0.05). The preoperative plan by the AIHIP system was completely realized in 25 patients (83.3%) of the trial group and the acetate templating was completely realized in 16 patients (53.3%) of the control group for the stem, showing significant difference ( P<0.05). There was no significant difference in the difference of bilateral femoral offsets, the difference of bilateral combined offsets, and the leg length discrepancy between the two groups before operation ( P>0.05). The difference of bilateral combined offsets at immediate after operation was significantly less in the trial group than in the control group ( t=-2.070, P=0.044); but there was no significant difference in the difference of bilateral femoral offsets and the leg length discrepancy between the two groups ( P>0.05).
Conclusion: Compared with the traditional 2D preoperative plan, the 3D preoperative plan by the AIHIP system is more accurate and detailed, especially in demonstrating the actual anatomical structures. In this study, the working flow of this artificial intelligent preoperative system was illustrated for the first time and preliminarily applied in THA. However, its potential clinical value needs to be discovered by advanced research.
目的: 研发一套人工智能辅助全髋关节置换术（total hip arthroplasty，THA）三维规划系统（AIHIP），并对其准确性进行初步临床应用验证。.
方法: 通过建立髋关节 CT 图像数据库，对髋关节 CT 图像进行手工标注，搭建深度学习神经网络，并使用已标注的 CT 图像对该神经网络进行训练，构建可视化交互界面，最终完成 AIHIP 系统。选择 2017 年 3 月—2020 年 5 月拟行单侧初次 THA 治疗的 60 例（60 髋）股骨头坏死患者，其中 30 例使用 AIHIP 系统（试验组）、30 例使用胶片模板测量方法（对照组）进行术前规划。两组患者年龄、性别、术侧及国际骨循环学会（ARCO）分期等一般资料比较，差异均无统计学意义（ P>0.05）。对比两组术中实际应用假体型号与术前规划假体型号符合情况，术前及术后即刻双侧股骨偏心距差值、双侧联合偏心距差值以及双下肢长度差值，初步评估该系统的准确性和有效性。.
结果: 试验组髋臼侧、股骨侧假体完全符合率分别为 90.0%（27/30）、83.3%（25/30），对照组为 56.7%（17/30）、53.3%（16/30），组间差异均有统计学意义（ P<0.05）。两组术前双侧股骨偏心距差值及双侧联合偏心距差值、双下肢长度差值比较，差异均无统计学意义（ P>0.05）；术后即刻试验组双侧联合偏心距差值明显小于对照组，差异有统计学意义（ t=–2.070， P=0.044），其余两指标组间差异均无统计学意义（ P>0.05）。.
结论: AIHIP 系统可用于 THA 术前规划，实现对髋关节的全面三维评估，与传统二维模板测量方法相比，具有更高的准确性和有效性，其潜在临床价值有待进一步研究证实。.
Keywords: Total hip arthroplasty; artificial intelligence; deep learning; preoperative plan; templating measurement.