Artificial intelligence in knee osteotomy: evolution of alignment assessment and surgical decision-making.

Authors

Ekrem Özdemir, Hüseyin Utku Özdeş

Published in

Journal of robotic surgery. Volume 20. Issue 1. Jul 31, 2026. Epub Jul 31, 2026.

Abstract

Knee osteotomy remains a fundamental joint-preserving intervention for unicompartmental osteoarthritis and lower limb malalignment, yet traditional two-dimensional planning is often limited by inter-observer variability and geometric imprecision. Over the past five years, artificial intelligence (AI) has transitioned from basic landmark detection into comprehensive surgical platforms encompassing automated alignment assessment, biomechanical simulation, and intraoperative navigation. This structured review, conducted according to PRISMA guidelines across PubMed, Embase, and Web of Science, evaluates the clinical integration of AI-driven reconstruction, biomechanical modeling, and prognostic algorithms. The results indicate that AI-driven workflows significantly enhance surgical precision; convolutional neural networks demonstrated mean absolute errors of less than 1.2 mm in landmark detection, while three-dimensional reconstructions achieved high anatomical fidelity with mechanical axis deviations averaging 0.52°±0.31°. Furthermore, biomechanical simulations utilizing recurrent neural networks proved substantially faster than traditional finite element analysis, and AI-enhanced robotic platforms achieved corrections within 1° of planned targets in over 95% of cases. Prognostic models reported ROC-AUC values up to 0.81, showing strong correlation with long-term joint survival. While statistical validation confirms high reproducibility across multicenter cohorts, the current literature is limited by a scarcity of prospective randomized controlled trials. In conclusion, AI in knee osteotomy has evolved into an essential tool for precision medicine, offering improved accuracy in patient-specific planning. Future research should prioritize explainable AI and digital twin platforms, alongside large-scale clinical trials, to definitively establish the impact of these technologies on long-term patient-reported outcomes and surgical success rates.

PMID:
42536128
Bibliographic data and abstract were imported from PubMed on 31 Jul 2026.

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