Haitao Li1, Jiangchang Xu1, Dingzhong Zhang1, Yaohua He2,3, Xiaojun Chen4,5. 1. Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Room A-925, Dongchuan Road 800, Minhang District, Shanghai, 200240, China. 2. Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai, 200233, China. heyaohua@sjtu.edu.cn. 3. Department of Orthopaedics, Jinshan Branch of Shanghai Sixth People's Hospital Affiliated to Shanghai University of Medicine & Health Sciences, 147 Jiankang Road, Shanghai, 201503, China. heyaohua@sjtu.edu.cn. 4. Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Room A-925, Dongchuan Road 800, Minhang District, Shanghai, 200240, China. xiaojunchen@sjtu.edu.cn. 5. Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China. xiaojunchen@sjtu.edu.cn.
Abstract
PURPOSE: Reverse shoulder arthroplasty (RSA) is an effective surgery for severe shoulder joint diseases. Traditionally, the preoperative planning procedure of RSA is manually conducted by experienced surgeons, resulting in prolonged operating time and unreliable drilling paths of the prosthetic fixation screws. In this study, an automatic surgical planning algorithm for RSA was proposed to compute the optimal path of screw implantation. METHODS: Firstly, a cone-shaped space containing alternative paths for each screw is generated using geometric parameters. Then, the volume constraint is applied to automatically remove inappropriate paths outside the bone boundary. Subsequently, the integral of grayscale value of the CT is used to evaluate the bone density and to compute the optimal solution. An automatic surgical planning software for RSA was also developed with the aforementioned algorithms. RESULTS: Twenty-four clinical cases were used for preoperative planning to evaluate the accuracy and efficiency of the system. Results demonstrated that the angles among the prosthetic fixation screws were all within constraint angle(45°), and the stability rate of the planned prosthesis was 94.92%. The average time for the automatic planning algorithm was 4.39 s, and 83.96 s for the whole procedure. Repetitive experiments were also conducted to demonstrate the robustness of our system, and the variance of the stability coefficient was 0.027%. CONCLUSIONS: In contrast to the cumbersome manual planning of the existing methods for RSA, our method requires only simple interaction operations. It enables efficient and precise automatic preoperative planning to simulate the ideal placement of the long prosthetic screws for the long-term stability of the prosthesis. In the future, it will have great clinical application prospects in RSA.
PURPOSE: Reverse shoulder arthroplasty (RSA) is an effective surgery for severe shoulder joint diseases. Traditionally, the preoperative planning procedure of RSA is manually conducted by experienced surgeons, resulting in prolonged operating time and unreliable drilling paths of the prosthetic fixation screws. In this study, an automatic surgical planning algorithm for RSA was proposed to compute the optimal path of screw implantation. METHODS: Firstly, a cone-shaped space containing alternative paths for each screw is generated using geometric parameters. Then, the volume constraint is applied to automatically remove inappropriate paths outside the bone boundary. Subsequently, the integral of grayscale value of the CT is used to evaluate the bone density and to compute the optimal solution. An automatic surgical planning software for RSA was also developed with the aforementioned algorithms. RESULTS: Twenty-four clinical cases were used for preoperative planning to evaluate the accuracy and efficiency of the system. Results demonstrated that the angles among the prosthetic fixation screws were all within constraint angle(45°), and the stability rate of the planned prosthesis was 94.92%. The average time for the automatic planning algorithm was 4.39 s, and 83.96 s for the whole procedure. Repetitive experiments were also conducted to demonstrate the robustness of our system, and the variance of the stability coefficient was 0.027%. CONCLUSIONS: In contrast to the cumbersome manual planning of the existing methods for RSA, our method requires only simple interaction operations. It enables efficient and precise automatic preoperative planning to simulate the ideal placement of the long prosthetic screws for the long-term stability of the prosthesis. In the future, it will have great clinical application prospects in RSA.