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

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-eTajrish 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 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.
 Level of evidence: IV

Keywords

Main Subjects

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        doi:10.1016/j.knee.2023.03.010.

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The Archives of Bone and Joint Surgery
Volume 13, Issue 7
July 2025
Pages 383-394

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History

  • Receive Date: 25 December 2024
  • Revise Date: 23 April 2025
  • Accept Date: 12 February 2025

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