Artificial intelligence-based versus conventional preoperative templating for tibial and femoral component size estimation in total knee arthroplasty: a systematic review and meta-analysis.
Authors
Affiliations (5)
Affiliations (5)
- Department of Orthopedics and Traumatology, Landeskrankenhaus Bludenz, Austria. Electronic address: [email protected].
- Department of Orthopedic Surgery, Massachusetts General Hospital, Boston, MA, USA; SUNY Upstate Medical University Norton College of Medicine, Syracuse, NY, USA.
- Independent Researcher, UK.
- Dr. Victorio de la Fuente Narváez Hospital, IMSS, Mexico.
- Instituto de Ortopedia e Traumatologia, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil.
Abstract
Preoperative templating supports total knee arthroplasty (TKA) planning, and AI-assisted tools are increasingly used to improve component-size estimation. However, the magnitude of improvement in exact femoral and tibial component-size prediction compared with conventional templating has not been quantitatively synthesized. We aimed to compare the accuracy of AI-assisted preoperative templating with conventional techniques in predicting exact implanted prosthesis size. PubMed (MEDLINE), Scopus, Web of Science, and the Cochrane Library were searched from inception to November 2025. Eligible studies included patients undergoing primary TKA in whom AI-assisted preoperative templating predicted exact femoral and/or tibial component size and was compared with conventional templating. Risk of bias was assessed using RoB 2 and ROBINS-I. Eight studies were included. In implanted-size-restricted analyses, AI-assisted templating did not significantly improve exact femoral component-size prediction (risk ratio (RR) 1.38; 95% confidence interval (CI) 0.92-2.08; P = 0.09; 95% prediction interval 0.63-3.04), whereas tibial prediction was borderline significant (RR 1.38; 95% CI 1.01-1.89; P = 0.05; 95% prediction interval 0.76-2.49). Computed tomography (CT)-based AI-assisted planning was associated with higher exact femoral and tibial prediction accuracy than conventional radiographic templating, while radiograph-based AI analyses were inconclusive. Short-term functional scores at 3 months after surgery showed no significant between-group differences. CT-based AI-assisted templating may improve exact component-size prediction in primary TKA. However, prediction intervals crossed the null, indicating uncertainty regarding the consistency and magnitude of benefit. The apparent advantage may reflect combined effects of three-dimensional imaging and AI-assisted planning rather than AI alone. Evidence regarding radiograph-based AI planning and postoperative benefit remains inconclusive.