سامانه مدیریت نشریات علمی دانشگاه علوم پزشکی مشهد

Khani, Yashar, Bisadi, Amir, Salmani, Ali, Namazi, Negarsadat, Elahi Vahed, Iman, Kianparsa, Joben, Nouroozi, Mohammad, Mansouri Rad, Fateme, Poursalehian, Mohammad. (1404). Can Artificial Intelligence Reliably and Accurately Measure Lower Limb Alignment: A Systematic Review and Meta-Analysis. , 13(7), 383-394. doi: 10.22038/abjs.2025.84846.3864
Yashar Khani; Amir Bisadi; Ali Salmani; Negarsadat Namazi; Iman Elahi Vahed; Joben Kianparsa; Mohammad Nouroozi; Fateme Mansouri Rad; Mohammad Poursalehian. "Can Artificial Intelligence Reliably and Accurately Measure Lower Limb Alignment: A Systematic Review and Meta-Analysis". , 13, 7, 1404, 383-394. doi: 10.22038/abjs.2025.84846.3864
Khani, Yashar, Bisadi, Amir, Salmani, Ali, Namazi, Negarsadat, Elahi Vahed, Iman, Kianparsa, Joben, Nouroozi, Mohammad, Mansouri Rad, Fateme, Poursalehian, Mohammad. (1404). 'Can Artificial Intelligence Reliably and Accurately Measure Lower Limb Alignment: A Systematic Review and Meta-Analysis', , 13(7), pp. 383-394. doi: 10.22038/abjs.2025.84846.3864
Khani, Yashar, Bisadi, Amir, Salmani, Ali, Namazi, Negarsadat, Elahi Vahed, Iman, Kianparsa, Joben, Nouroozi, Mohammad, Mansouri Rad, Fateme, Poursalehian, Mohammad. Can Artificial Intelligence Reliably and Accurately Measure Lower Limb Alignment: A Systematic Review and Meta-Analysis. , 1404; 13(7): 383-394. doi: 10.22038/abjs.2025.84846.3864

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

The Archives of Bone and Joint Surgery
مقاله 1، دوره 13، شماره 7، مهر 2025، صفحه 383-394    اصل مقاله (1.17 M)
نوع مقاله: SYSTEMATIC REVIEW
شناسه دیجیتال (DOI): 10.22038/abjs.2025.84846.3864
نویسندگان
Yashar Khani1؛ Amir Bisadi* 2؛ Ali Salmani3؛ Negarsadat Namazi1؛ Iman Elahi Vahed1؛ Joben Kianparsa4؛ Mohammad Nouroozi5؛ Fateme Mansouri Rad6؛ Mohammad Poursalehian7
1Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2Department of Orthopedic Surgery, Akhtar Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3Student Research Committee, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
4Student Research Committee, School of Medicine, Shahed University, Tehran, Iran
5Clinical Research Development Unit (CRDU), Shohada-eTajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
6Birjand University of Medical Sciences and Health Services, Birjand, Iran
7Joint Reconstruction Research Center, Tehran University of Medical Sciences, Tehran, Iran
چکیده
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
کلیدواژه‌ها
Artificial intelligence؛ Hip-knee-ankle angle؛ Joint line congruency angle؛ Lower limb alignment؛ Mechanical axis deviation؛ Neural network
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