Objective: To explore the short-term effectiveness of hip revision surgery guided by artificial intelligence preoperative planning (AIHIP) system.
Methods: The clinical data of 22 patients (23 hips) who were admitted between June 2019 and March 2023 and met the selection criteria were retrospectively analyzed. There were 12 males and 10 females with an average age of 69.7 years (range, 44-90 years). There were 19 hips in the first revision, 3 hips in the second revision, and 1 hip in the third revision. The causes of revision included 12 hips with prosthesis loosening, 4 hips with acetabular cup loosening, 3 hips with osteolysis, 2 hips with acetabular dislocation, 1 hip with postoperative infection, and 1 hip with prosthesis wear. There were 6 hips in stage ⅡA, 9 hips in stage ⅡB, 4 hips in stage ⅡC, 3 hips in stage ⅢA, and 1 hip in stage ⅢB according to Paprosky staging of acetabular bone defect. The replacement of prosthesis type, operation time, hospitalization stay, ground active condition, and postoperative infection, fracture, prosthesis loosening, and other adverse events were recorded. The function of the affected limb was evaluated by Harris score before operation, at 1 week and 6 months after operation, and the range of motion of the hip joint was compared before operation and at 6 months after operation.
Results: The operation time was 85-510 minutes, with an average of 241.8 minutes; the hospitalization stay was 7-35 days, with an average of 15.2 days; the time of disassociation from the walker was 2-108 days, with an average of 42.2 days. All the 22 patients were followed up 8-53 months (mean, 21.7 months). No adverse events such as prosthesis loosening or infection occurred in the rest of the patients, except for postoperative hematoma of the thigh in 1 patient and dislocation of the hip in 1 hip. The matching degree of acetabular cup was completely matched in 22 hips and mismatched in 1 hip (+2), the matching rate was 95.65%. The matching degree of femoral stem was completely matched in 22 hips and generally matched in 1 hip (-1), and the matching rate was 100%. The Harris scores were 55.3±9.8 and 89.6±7.2 at 1 week and 6 months after operation, respectively, which significantly improved when compared with before operation (33.0±8.6, P<0.05), and further improved at 6 months after operation than at 1 week after operation ( P<0.05). The function of hip joint was evaluated by Harris score at 6 months after operation, and 21 hips were good and 2 hips were moderate, which could meet the needs of daily life. The range of motion of hip joint was (111.09±10.11)° at 6 months after operation, which was significantly different from (79.13±18.50)° before operation ( t=-7.269, P<0.001).
Conclusion: AIHIP system can improve the accuracy of revision surgery, reduce the difficulty of surgery, and achieve good postoperative recovery and satisfactory short-term effectiveness.
目的: 探究人工智能术前规划系统（AIHIP 系统）辅助髋关节翻修手术的早期疗效。.
方法: 回顾分析 2019 年 6月—2023年3月收治且符合选择标准的22例（23髋）髋关节翻修患者临床资料。其中男12例，女10例；年龄 44～90岁，平均 69.7 岁。初次翻修19髋，2次翻修3髋，3次翻修1髋。翻修原因：假体松动12髋，髋臼杯松动4髋，骨溶解3髋，髋臼脱位2髋，术后感染1髋，假体磨损1髋。Paprosky髋臼骨缺损分期：ⅡA期6髋，ⅡB期9髋，ⅡC期4髋，ⅢA期3髋，ⅢB期1髋。记录患者更换假体型号、手术时间、住院时间、下地情况等，以及术后感染、骨折、假体松动等不良事件发生情况。术前、术后1周及6个月采用 Harris评分评价患肢功能，比较术前及术后6个月髋关节活动度。.
结果: 患者手术时间 85～510 min，平均 241.8 min；住院时间 7～35 d，平均 15.2 d；脱离助行器时间 2～108 d，平均42.2 d。22例患者均获随访，随访时间8～53个月，平均21.7个月。除1例患者术后出现大腿血肿、1髋发生髋关节脱位外，余患者均无假体松动、感染等不良事件发生。术后髋臼杯型号匹配程度为完全匹配22髋，不匹配1髋（+2号），匹配率95.65%；股骨柄型号匹配程度为完全匹配22例髋、一般匹配1髋（–1号），匹配率100%。术后1周及6个月 Harris 评分分别为（55.3±9.8）分和（89.6±7.2）分，较术前（33.0±8.6）分显著改善（ P<0.05），术后6个月较1周时进一步改善（ P<0.05）；术后6个月根据Harris 评分评价患者髋关节功能，获良21髋、中2髋，可满足日常生活需求。术后6个月髋关节活动度为（111.09±10.11）°，与术前（79.13±18.50）° 比较差异有统计学意义（ t=−7.269， P<0.001）。.
结论: AIHIP 系统辅助治疗THA术后翻修患者，可提升翻修手术的精准性，降低手术难度，患者术后恢复效果好，早期疗效满意。.
Keywords: Artificial intelligence; preoperative planning; revision surgery; total hip arthroplasty.