Dong Wu1, Xingyu Liu2, Yiling Zhang3, Jiying Chen4, Peifu Tang4, Wei Chai4. 1. 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. 2. School of Life Sciences, Tsinghua University, Beijing, 100084, P.R.China. 3. Longwood Valley Medical Technology Co. Ltd., Beijing, 100190, P.R.China. 4. Department of Orthopedics, the First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, P.R.China.
Abstract
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.
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.
Entities:
Keywords:
Total hip arthroplasty; artificial intelligence; deep learning; preoperative plan; templating measurement
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