Wu Dong, Liu Xingyu, Zhang Yiling, Chen Jiying, Tang Peifu, Chai Wei
Department of Orthopedics, the First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, P.R.China;The Medical District South of Beijing, Chinese PLA General Hospital, Beijing, 100071, P.R.China.
School of Life Sciences, Tsinghua University, Beijing, 100084, P.R.China.
Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi. 2020 Sep 15;34(9):1077-1084. doi: 10.7507/1002-1892.202005007.
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.
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 ( =30) and the traditional acetate templating was applied in the control group ( =30). There was no significant difference in age, gender, operative side, and Association Research Circulation Osseous (ARCO) grading between the two groups ( >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.
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 ( <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 ( <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 ( >0.05). The difference of bilateral combined offsets at immediate after operation was significantly less in the trial group than in the control group ( =-2.070, =0.044); but there was no significant difference in the difference of bilateral femoral offsets and the leg length discrepancy between the two groups ( >0.05).
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.
开发一种基于人工智能的全髋关节置换术(THA)三维(3D)术前规划系统(AIHIP),并通过初步临床应用验证其准确性。
建立由手动分割的CT图像序列组成的CT图像数据库,以训练自主研发的深度学习神经网络。深度学习神经网络和术前规划模块组装在一个视觉交互界面——AIHIP中。之后,纳入2017年3月至2020年5月期间60例单侧初次全髋关节置换术患者(60髋),并分为两组。试验组(n = 30)应用AIHIP系统,对照组(n = 30)应用传统醋酸酯模板法。两组患者在年龄、性别、手术侧别和骨循环研究协会(ARCO)分级方面无显著差异(P > 0.05)。比较两组的符合率、术前和术后下肢长度差异、双侧股骨偏心距差异、双侧联合偏心距差异,以评估AIHIP系统的准确性和效率。
试验组27例患者(90.0%)的髋臼杯术前规划通过AIHIP系统完全实现,对照组17例患者(56.7%)通过醋酸酯模板法完全实现,差异有统计学意义(P < 0.05)。试验组25例患者(83.3%)的股骨柄术前规划通过AIHIP系统完全实现,对照组16例患者(53.3%)通过醋酸酯模板法完全实现,差异有统计学意义(P < 0.05)。两组术前双侧股骨偏心距差异、双侧联合偏心距差异和下肢长度差异无显著差异(P > 0.05)。术后即刻试验组双侧联合偏心距差异显著小于对照组(t = -2.070,P = 0.044);但两组双侧股骨偏心距差异和下肢长度差异无显著差异(P > 0.05)。
与传统二维术前规划相比,AIHIP系统的三维术前规划更准确、详细,尤其是在显示实际解剖结构方面。本研究首次阐述了这种人工智能术前系统的工作流程,并初步应用于全髋关节置换术。然而,其潜在的临床价值需要通过进一步研究来发现。