Can Artificial Intelligence Reliably and Accurately Measure Lower Limb Alignment: A Systematic Review and Meta-Analysis.

Abstract

Objectives: Lower limb alignment (LLA) measurements are vital for pre-operative assessments and surgical planning in orthopedics. Artificial intelligence (AI) can enhance the precision and consistency of these measurements. 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 in 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 the 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 the potential of AI for clinical applications while also identifying areas that require refinement and standardization.

Conclusion: AI exhibits high reliability and accuracy in measuring key LLA angles, often outperforming manual techniques in both speed and consistency. It holds significant 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.