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The Archives of Bone and Joint Surgery، جلد ۱۳، شماره ۷، صفحات ۳۸۳-۳۹۴
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عنوان انگلیسی Can Artificial Intelligence Reliably and Accurately Measure Lower Limb Alignment: A Systematic Review and Meta-Analysis
چکیده انگلیسی مقاله 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
کلیدواژه‌های انگلیسی مقاله Artificial intelligence, Hip-knee-ankle angle, Joint line congruency angle, Lower limb alignment, mechanical axis deviation, Neural Network
نویسندگان مقاله | Yashar Khani
Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran


| Amir Bisadi
Department of Orthopedic Surgery, Akhtar Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran


| Ali Salmani
Student Research Committee, Shahid Sadoughi University of Medical Sciences, Yazd, Iran


| Negarsadat Namazi
Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran


| Iman Elahi Vahed
Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran


| Joben Kianparsa
Student Research Committee, School of Medicine, Shahed University, Tehran, Iran


| Mohammad Nouroozi
Clinical Research Development Unit (CRDU), Shohada-eTajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran


| Fateme Mansouri Rad
Birjand University of Medical Sciences and Health Services, Birjand, Iran


| Mohammad Poursalehian
Joint Reconstruction Research Center, Tehran University of Medical Sciences, Tehran, Iran
نشانی اینترنتی https://abjs.mums.ac.ir/article_26031.html
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زبان مقاله منتشر شده en
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نوع مقاله منتشر شده SYSTEMATIC REVIEW
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