ObjectiveTo 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.MethodsThe 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.ResultsThe 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).ConclusionCompared 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 systematically review the epidemic trend and disease burden of ischemic stroke in the Chinese population and to provide references for formulating reasonable prevention and treatment measures and allocating health resources. Methods Based on Global Burden of Disease (GBD) data, we analyzed the morbidity, mortality, disability-adjusted life year (DALY) and normalized rates for ischemic stroke in China from 1990 to 2019 and evaluated the changes in the disease burden by sex and age group. Meanwhile, joinpoint regression model was constructed to analyze the time trend change in each stage during the study period. Results Compared with 1990, the incidence, mortality and DALY rate of ischemic stroke in China increased by 171.68%, 125.60% and 98.60% in 2019, among which the incidence, mortality and DALY rate of males increased by 184.29%, 148.96% and 115.16%, respectively; the morbidity, mortality and DALY rates of females increased by 160.9%, 101.32% and 81.44%, respectively. The age-standardized incidence increased by 34.70%, while the age-standardized mortality and age-standardized DALY rate decreased by 3.33% and 4.02%, respectively; the age-standardized incidence, mortality and DALY rates of males increased by 39.52%, 8.03% and 3.68%, respectively; the age-standardized incidence rate of females increased by 31.40%, while the age-standardized mortality rate and age-standardized DALY rate decreased by 14.02% and 11.53%, respectively. In 2019, both the mortality rate and DALY rate due to ischemic stroke increased with age, and the highest rate was found in the population over 85 years old. Males over 60 years old were significantly than females. In the 55-84 age group, the incidence of ischemic stroke in females was higher than that in males, while in the 85 and above age group, the incidence of ischemic stroke in females was lower than that in males. The AAPC of age-standardized incidence, age-standardized mortality, and age-standardized DALY rates due to ischemic stroke from 1990 to 2019 were 1.06% (95%CI 1.00% to 1.11%), 0.01% (95%CI −0.45% to 0.48%) and −0.16% (95%CI −0.53% to 0.22%), respectively. All indicators of the AAPC for males were higher than those for females. ConclusionThe curvent age-standardized mortality and DALY rate of ischemic stroke in China have decreased slightly compared with 1990. The crude mortality, morbidity and disease burden have significantly increased. All indicators of the AAPC for males were higher than those for females. To reduce the epidemic trend and disease burden of ischemic stroke, reasonable prevention and treatment measures and rational allocation of health resources should be made according to sex and age.
ObjectiveTo investigate the early effectiveness of artificial intelligence (AI) assisted total hip arthroplasty (THA) system (AIHIP) in the treatment of patients with Crowe type Ⅳ developmental dysplasia of the hip (DDH).MethodsThe clinical data of 23 patients with Crowe type Ⅳ DDH who met the selection criteria between May 2019 and December 2020 were retrospectively analyzed. There were 3 males and 20 females, the age ranged from 44 to 74 years, with an average of 52.65 years. The absolute value of the lower limbs discrepancy before operation was (15.17±22.17) mm. The preoperative Harris score was 62.4±7.2. The AIHIP system was used for preoperative planning, and the operations were all performed via conventional posterolateral approach. Thirteen patients with difficulty in reduction during operation underwent subtrochanteric shortening osteotomy (SSOT). The operation time, hospital stay, and adverse events were recorded; Harris score was used to evaluate the function of the affected limb at 1 day before operation and 1 week and 6 months after operation; pelvic anteroposterior X-ray film was taken at 1 day after operation to evaluate the position of the prosthesis. The matching degree of prosthesis was evaluated according to the consistency of intraoperative prosthesis model and preoperative planning.ResultsThe matching degree of acetabular cup model after operation was 16 cases of perfect matching, 4 cases of general matching (1 case of +1, 3 cases of –1), and 3 cases of mismatch (all of them were +2), the coincidence rate was 86.96%. The matching degree of femoral stem model was perfect matching in 22 cases and general matching in 1 case of –1, and the coincidence rate was 100%. One patient had a periprosthesis fracture during operation, and was fixed with a wire cable during operation, and walked with the assistance of walking aid at 6 weeks after operation; the rest of the patients walked with the assistance of walking aid at 1 day after operation. The operation time was 185-315 minutes, with an average of 239.43 minutes; the hospital stay was 8-20 days, with an average of 9.96 days; and the time of disengagement from the walking aid was 2-56 days, with an average of 5.09 days. All patients were followed up 6 months. All incisions healed by first intension, and there was no complication such as infection, dislocation, refracture, and lower extremity deep venous thrombosis; X-ray films at 1 day and 6 months after operation showed that the acetabular and femoral prostheses were firmly fixed and within the safe zone; the absolute value of lower limbs discrepancy at 1 day after operation was (11.96±13.48) mm, which was not significantly decreased compared with that before operation (t=0.582, P=0.564). All osteotomies healed at 6 months after operation. The Harris scores at 1 week and 6 months after operation were 69.5±4.9 and 79.2±5.7 respectively, showing significant differences between pre- and post-operation (P<0.05). At 6 months after operation, the hip function was evaluated according to Harris score, and 13 cases were good, 9 cases were fair, and 1 case was poor.ConclusionAIHIP system-assisted THA (difficult to reposition patients combined with SSOT) for adult Crowe type Ⅳ DDH has high preoperative planning accuracy, easy intraoperative reduction, early postoperative landing, and satisfactory short-term effectiveness.
Objective To develop a neural network architecture based on deep learning to assist knee CT images automatic segmentation, and validate its accuracy. Methods A knee CT scans database was established, and the bony structure was manually annotated. A deep learning neural network architecture was developed independently, and the labeled database was used to train and test the neural network. Metrics of Dice coefficient, average surface distance (ASD), and Hausdorff distance (HD) were calculated to evaluate the accuracy of the neural network. The time of automatic segmentation and manual segmentation was compared. Five orthopedic experts were invited to score the automatic and manual segmentation results using Likert scale and the scores of the two methods were compared. Results The automatic segmentation achieved a high accuracy. The Dice coefficient, ASD, and HD of the femur were 0.953±0.037, (0.076±0.048) mm, and (3.101±0.726) mm, respectively; and those of the tibia were 0.950±0.092, (0.083±0.101) mm, and (2.984±0.740) mm, respectively. The time of automatic segmentation was significantly shorter than that of manual segmentation [(2.46±0.45) minutes vs. (64.73±17.07) minutes; t=36.474, P<0.001). The clinical scores of the femur were 4.3±0.3 in the automatic segmentation group and 4.4±0.2 in the manual segmentation group, and the scores of the tibia were 4.5±0.2 and 4.5±0.3, respectively. There was no significant difference between the two groups (t=1.753, P=0.085; t=0.318, P=0.752). Conclusion The automatic segmentation of knee CT images based on deep learning has high accuracy and can achieve rapid segmentation and three-dimensional reconstruction. This method will promote the development of new technology-assisted techniques in total knee arthroplasty.