Comparison of Artificial Intelligence and Traditional Methods in Preoperative Planning for Primary Total Hip Arthroplasty: A Systematic Review and Meta-Analysis.
Authors: Xue Di , Wang Kaiyong , He Huan , Wang Liru , Dai Yupei , Shen Guohang , Chen Yang , Su Yajing , Lin Xue 
Published: January 2025

Abstract

Background
The application of artificial intelligence (AI) in orthopedics is becoming increasingly widespread, particularly in the diagnosis and treatment of hip-related diseases. Although AI-assisted total hip arthroplasty (THA) techniques have reached a relatively mature stage, their specific role in preoperative planning for THA remains in the research phase. Current studies are generally small in scale, and their findings appear somewhat fragmented, making it difficult to draw definitive conclusions. Against this backdrop, a systematic review and meta-analysis on the application of AI in THA preoperative planning may provide a more comprehensive and rational answer.
Questions/purposes
Compared to traditional methods, does artificial intelligence (AI) offer more and better advantages in preoperative planning for patients undergoing primary total hip arthroplasty (THA)? Does it possess potential for future development?
Methods
We conducted a comprehensive and systematic search in the PubMed, Embase, Web of Science, and Cochrane Library databases, covering the period from their inception to October 30, 2024. This study adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and has been registered in PROSPERO[1]. The included studies focused on patients undergoing primary total hip arthroplasty (THA), with the experimental group using artificial intelligence (AI) for preoperative planning and the control group employing traditional planning methods. We excluded the following: papers published on preprint servers, unpublished studies, conference abstracts, and studies registered on ClinicalTrials.gov but not yet published. Ultimately, data were extracted from 15 eligible studies.
To assess the methodological quality of the studies, we applied bias risk assessment methods based on the type of study. The revised Cochrane Risk of Bias tool was employed to assess potential bias in randomized controlled trials (RCTs). For non-randomized controlled trials, including retrospective cohort studies, retrospective case-control studies, and prospective cohort studies, we employed the Newcastle-Ottawa Scale (NOS) for bias risk assessment. Due to the high heterogeneity among studies (I² > 50%), a random-effects model was used for the analysis.
Results
In the 15 studies that met the inclusion criteria, a total of 2572 participants were included. These patients required primary total hip arthroplasty (THA) due to various hip diseases. Among them, 1307 patients in the experimental group used artificial intelligence (AI) for preoperative planning, while 1265 patients in the control group used traditional methods. There were no statistically significant differences in the baseline characteristics of the included patients (such as age, BMI, preoperative leg length discrepancy, and preoperative Harris score) (P≥0.05), which ensures the reliability of the predictive results.
According to the data summary and analysis, compared with traditional methods, AI showed superior performance in the following aspects: the odds ratio (OR) for acetabular component matching accuracy was 0.26 (95% CI, 0.20–0.34; P=0.009; I²=58%), and for femoral component matching accuracy, the OR was 0.25 (95% CI, 0.19–0.32; P=0.66; I²=0%). The matching accuracy was defined with a size difference as the acceptable margin of error. The mean difference (MD) for postoperative leg length discrepancy was -0.49 (95% CI, -0.59 to -0.39; P<0.0001; I²=77%), the MD for surgical time was -16.07 (95% CI, -18.00 to -14.14; P<0.00001; I²=96%), the MD for intraoperative blood loss was -45.91 (95% CI, -61.03 to -30.78; P=0.04; I²=61%), and the MD for postoperative Harris score was 0.83 (95% CI, 0.38–1.28; P=0.001; I²=70%). In addition, the OR for acetabular cup prosthesis prediction accuracy was 0.82 (95% CI, 0.51–1.34; P=0.0001; I²=89%), and the overall average prediction accuracy had an OR of 0.25 (95% CI, 0.18–0.35; P=0.93; I²=0%).
Conclusion
The results of this systematic review and meta-analysis indicate that artificial intelligence (AI) performs comparably to, or even better than, traditional methods in preoperative planning for hip arthroplasty. Compared with traditional methods, the AI group demonstrated advantages such as reducing surgical time, minimizing intraoperative blood loss, lowering surgical risks, and decreasing surgical trauma. These benefits help promote rapid postoperative recovery, shorten hospital stays, and reduce the occurrence of complications. Additionally, patients in the AI group had higher postoperative Harris scores, less postoperative pain, faster functional recovery, and better postoperative adaptation. AI-assisted preoperative planning for total hip arthroplasty (THA) also improves the accuracy of hip component matching prediction, reduces the likelihood of errors in clinical decision-making, effectively alleviates tensions in the doctor-patient relationship, and reduces the waste of medical resources.