Advantages and effectiveness of AI three-dimensional reconstruction technology in the preoperative planning of total hip arthroplasty.

Journal: Scientific reports
Published Date:

Abstract

In order to explore the application effect of artificial intelligence (AI) 3D reconstruction technology in total hip arthroplasty (THA), this study included a total of 109 patients with unilateral femoral head ischemic necrosis. According to the preoperative planning method, they were divided into the AI group (n = 55) and the 2D group (n = 54). The operating time, intraoperative bleeding, length of hospital stay length, prosthesis conformity, imaging indicators and Harris scores. The complete conformity rates of the acetabular cup and femoral stem in the AI group (90.9% and 87.3%) were significantly higher than those in the 2D group (72.2% and 66.7%) (P < 0.05). The perioperative indicators of the AI group, such as operating time intraoperative bleeding volume and length of hospital stay were all better than those of the 2D group (P < 0.05). The AI group had significantly less postoperative leg length discrepancy (LLD) than the 2D group (P < 0.05). The Harris score at 1 month, 3 months and 6 months after surgery was significantly higher in the AI group than in the 2D group, and the difference was statistically significant (P < 0.05). Using AI 3D reconstruction technology to perform preoperative planning for patients scheduled to undergo THA can assist clinicians in completing the surgery more quickly and accurately, effectively control the patient's postoperative LLD, and also reduce intraoperative bleeding, shorten the patient's hospital stay, and accelerate the patient's postoperative functional recovery.

Authors

  • Shulin Li
    Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
  • Jilin Jiang
    College of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Jiahao Zhang
    Department of Thoracic Surgery, Ruijin Hospital, Shanghai, China; Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Laipeng Yan
    Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
  • Huiling Guo
    Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
  • Faqiang Tang
    Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China. faqiangtang@fjmu.edu.cn.