Document Type : SYSTEMATIC REVIEW
Authors
1
Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2
Department of Orthopedic Surgery, Akhtar Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
3
Student Research Committee, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
4
Student Research Committee, School of Medicine, Shahed University, Tehran, Iran.
5
Clinical Research Development Unit (CRDU), Shohada-e-Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
6
Birjand University of Medical Sciences and Health Services, Birjand, Iran.
7
Joint Reconstruction Research Center, Tehran University of Medical Sciences, Tehran, Iran.
10.22038/abjs.2025.84846.3864
Abstract
Objectives. Lower limb alignment (LLA) measurements are vital for pre-operative assessments and surgical planning in orthopedics. Artificial intelligence (AI) can enhance these measurements' efficiency, precision, and consistency. This systematic review and meta-analysis evaluates the accuracy and reliability of AI-based approaches in detecting anatomical landmarks and measuring LLA angles, highlighting both their strengths and limitations.
Methods. Adhering to PRISMA guidelines, we searched PubMed, Scopus, Embase, and Web of Science on July 2024 and included observational studies validating AI-driven LLA measurements. Pooled intraclass correlation coefficients (ICCs) were computed to assess inter-rater reliability between AI and manual measurements. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to assess study quality.
Results. We reviewed 28 studies with 47,200 patients and 61,253 images; AI demonstrated high reliability for measuring 15 lower limb angles, with pooled ICCs ranging from 0.9811 to 1.0597. Angles like the hip-knee-ankle (HKA; ICC = 0.9987, 95% CI: 0.9975–0.9998) and mechanical tibiofemoral angle (mTFA; ICC = 1.0001, 95% CI: 1.0001–1.0001) showed near-perfect agreement. In contrast, the joint line convergence angle (JLCA) and femoral anatomical-mechanical angle (FAMA) exhibited lower reliability and significant publication bias. Heterogeneity was substantial across most angles (I² = 63%–100%). These findings highlight AI's potential for clinical application while identifying areas that require refinement and standardization.
Conclusion. AI exhibits high reliability and accuracy in measuring key LLA angles, often outperforming manual techniques in speed and consistency. It holds great promise as a clinical tool, though challenges with less reliable angles warrant further refinement. Future studies should focus on standardizing landmark definitions and addressing implementation barriers to maximize AI’s potential in orthopedic practice.
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