Image-Based Robotic Unicompartmental Knee Arthroplasty Results in Fewer Radiologic Outliers with No Impact on Revision Rates Compared to Imageless Systems: A Systematic Review

摘要

Background: Robotic-assisted unicompartmental knee arthroplasty (UKA) enhances the precision of component alignment compared to conventional techniques. Although various robotic systems exist, direct comparisons assessing their relative clinical performance remain limited. The purpose of this study is to provide a comparison between image-based and imageless robotic UKA. Methods: A systematic review was conducted in accordance with PRISMA guidelines. Five databases were searched: PubMed (via MEDLINE), Epistemonikos, Cochrane Library, Web of Science, and Scopus. Inclusion criteria were (1) studies comparing rUKA and cUKA with radiologic parameters and revision rates (prospective or retrospective), (2) human subjects, (3) meta-analyses for cross-referencing, and (4) English language. Data collected included (1) pre- and postoperative radiologic parameters, (2) radiologic outliers, and (3) revisions and their causes. A random-effects meta-analysis was employed to enable a generalizable comparison. Mean differences (MDs) with 95% confidence intervals (CIs) were calculated for continuous variables, and log odds ratios (LORs) with 95% CIs for binary outcomes. Results: Image-based robotic UKA was associated with fewer joint line height outliers (LOR = 3.5, 95% CI: 0.69–6.30, p = 0.015) using a 2° threshold. HKA outliers (thresholds 2–3°) were also reduced (LOR = 0.6, 95% CI: 0.09–1.19, p = 0.024). Posterior tibial and posterior femoral implant fit were significantly lower with image-based systems (LOR = 1.7, 95% CI: 1.37–2.03, respectively, LOR = 1.7, 95% CI: 1.29–1.91; p < 0.001 for both). No significant differences in revision rates were observed. Conclusions: Image-based robotic systems may result in fewer outliers in key radiologic parameters, including hip–knee angle, joint-line height, posterior tibial, and posterior femoral fit, though reporting remains highly heterogeneous.