Efficacy of an artificial intelligence preoperative planning system for assisting in revision surgery after artificial total hip arthroplasty

Abstract

Objective: To explore the early efficacy of an artificial intelligence preoperative planning system (AIHIP system) for assisting in hip revision surgery.

Methods: The clinical data of 25 patients (26 hips) who underwent hip revision between June 2019 and December 2023 and who met the selection criteria were retrospectively analyzed. There were 13 males and 12 females; the ages ranged from 44 to 90 years, with a mean of 69.1 years. The patients' replacement of prosthesis model, operation time, hospitalization time, postoperative time out of bed, etc., as well as the occurrence of adverse events such as postoperative infection, fracture, and loosening of the prosthesis were recorded. The Harris Hip score (HHS) was used to evaluate the function of the affected limbs preoperatively, and 1 week and 6 months postoperatively, and hip mobility was compared preoperatively and 6 months postoperatively.

Results: All 25 patients were followed up for 6 to 59 months, with an average of 25.3 months. Except for one patient who developed a thigh hematoma (treated with incision and drainage and decompression) and hip dislocation in one hip (repaired), the remaining patients experienced no adverse events such as loosening of the prosthesis or infection. The postoperative acetabular cup type matching degree completely matched 25 hips, not matching 1 hip (+ 2 number), for a matching rate of 96.15%; the femoral stem type matching degree completely matched 25 hips, generally matching 1 hip (-1 number), for a matching rate of 100%. The Harris scores were 54.7 ± 9.6 and 89.6 ± 7.0 at 1 week and 6 months after surgery, respectively, which were significantly improved (P < 0.05) compared with the preoperative scores of 33.5 ± 8.3, and further improved at 6 months after surgery compared with the 1-week period (P < 0.05). The patients' hip function was evaluated according to the Harris score at 6 months after surgery, and they were assigned to 23 good hips and 3 medium hips, which could satisfy daily life needs. Hip mobility at 6 months after surgery was 111.15 ± 9.72°, and the difference was statistically significant compared with the preoperative value of 79.42 ± 17.51° (t = -8.077, P < 0.001).

Conclusion: AIHIP system-assisted treatment of THA postoperative revision patients can improve the precision of revision surgery, and reduce the difficulty of surgery, in patients with good postoperative recovery and satisfactory early outcomes.

Keywords: Artificial intelligence; Preoperative planning; Revision surgery; Total hip arthroplasty.

Fig. 1

Flowchart of the preoperative planning of the AIHIP system

Fig. 2

Individual examples of preoperative planning with the AIHIP system a AI calculation of acetabular cup placement; b-e Acetabular side details; f Simulated postoperative anterior 3D view; g Simulated postoperative posterior 3D view; h Simulated postoperative overall 3D view; i Simulated anteroposterior radiographs of the hip joint

Fig. 3

Matching of AI-designed prostheses with actual surgical prostheses

Fig. 4

Harris score and changes in joint mobility

Fig. 5

Patient, 74 years old, postoperative loosening of the bioprosthesis with periprosthetic fracture of the lateral femur, major revision of the lateral femur a. Preoperative; b. Installation of cemented mortise cups; c. Splitting of the lateral femoral muscle to expose the distal femur; d. Kirschner's pin drilling to open the window; e. Postoperative

Fig. 6

Patient, male, 46 years old, right hip Paprosky stage IIC bone defect with total revision of hip prosthesis a. Preoperative; b. Preoperative CT sagittal; c. Postoperative

Fig. 7

Patient, female, 75 years old, biologic prosthesis, primary revision acetabular side a. Preoperative; b. Preoperative CT coronal; c. Postoperative