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Purpose <p>To develop, validate, and benchmark a fully automated deep learning (DL) system that simultaneously measures 15 coronal lower-limb alignment parameters on standing long-leg radiographs (LLRs) and localizes deformity, and to compare two tibial joint-line definitions for suitability in DL-based measurement.</p> Methods <p>A retrospective set of 309 anteroposterior standing LLRs was split into training/validation/testing (60/20/20). External generalizability was assessed using 75 independent LLRs from a different scanner and patient cohort. YOLOv5 was used to detect multiple bony landmarks, followed by algorithmic calculation of 15 parameters (e.g., HKAA, mLDFA, mMPTA). Two tibial joint-line definitions were evaluated: Method 1 (line through medial/lateral lowest tibial plateau points) and Method 2 (line through most medial/lateral plateau edges). Accuracy, clinical failure rate (≥ 2° or ≥ 2%), and runtime were compared with expert consensus annotations. Bland-Altman analysis was added to assess measurement bias and clinical agreement.</p> Results <p>On the external dataset, absolute errors for knee phenotype–related parameters ranged from 0.07° to 0.45°. Automated analysis of all 15 parameters took 24.3 ± 0.7&#xa0;s, reducing time by 89–91% versus manual measurement (<i>p</i> &lt; 0.001). Method 2 produced significantly smaller absolute errors than Method 1 for nearly all parameters (<i>p</i> ≤ 0.005). Clinically significant failure rates were low (0–4.7%) and were significantly lower than an attending physician’s for several key metrics on the external set. The distribution of varus/valgus/neutral alignment based on HKAA and extraarticular deformity locations were reported.</p> Conclusion <p>This DL framework provides fast, comprehensive, and specialist-level coronal alignment assessment on LLRs. An edge-based tibial joint-line definition (Method 2) outperforms a lowest-point definition, improving precision and reliability for DL measurement pipelines, supporting clinically deployable orthopedic imaging AI. Future prospective studies are warranted to validate Method 2 against clinical outcomes including osteoarthritis progression and surgical results.</p>

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Automated assessment of coronal lower extremity alignment on long-leg radiographs using a deep-learning model: validation, efficiency gains, and superiority of an edge-based tibial joint-line definition

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月26日

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  • Wenzhong Jin,
  • Xu Jiang,
  • Yushun Tao,
  • Yuqi Hu,
  • Mengning Yan,
  • Liangbin Gao,
  • Kai Xie,
  • Liao Wang,
  • Jinwu Wang

摘要

Purpose

To develop, validate, and benchmark a fully automated deep learning (DL) system that simultaneously measures 15 coronal lower-limb alignment parameters on standing long-leg radiographs (LLRs) and localizes deformity, and to compare two tibial joint-line definitions for suitability in DL-based measurement.

Methods

A retrospective set of 309 anteroposterior standing LLRs was split into training/validation/testing (60/20/20). External generalizability was assessed using 75 independent LLRs from a different scanner and patient cohort. YOLOv5 was used to detect multiple bony landmarks, followed by algorithmic calculation of 15 parameters (e.g., HKAA, mLDFA, mMPTA). Two tibial joint-line definitions were evaluated: Method 1 (line through medial/lateral lowest tibial plateau points) and Method 2 (line through most medial/lateral plateau edges). Accuracy, clinical failure rate (≥ 2° or ≥ 2%), and runtime were compared with expert consensus annotations. Bland-Altman analysis was added to assess measurement bias and clinical agreement.

Results

On the external dataset, absolute errors for knee phenotype–related parameters ranged from 0.07° to 0.45°. Automated analysis of all 15 parameters took 24.3 ± 0.7 s, reducing time by 89–91% versus manual measurement (p < 0.001). Method 2 produced significantly smaller absolute errors than Method 1 for nearly all parameters (p ≤ 0.005). Clinically significant failure rates were low (0–4.7%) and were significantly lower than an attending physician’s for several key metrics on the external set. The distribution of varus/valgus/neutral alignment based on HKAA and extraarticular deformity locations were reported.

Conclusion

This DL framework provides fast, comprehensive, and specialist-level coronal alignment assessment on LLRs. An edge-based tibial joint-line definition (Method 2) outperforms a lowest-point definition, improving precision and reliability for DL measurement pipelines, supporting clinically deployable orthopedic imaging AI. Future prospective studies are warranted to validate Method 2 against clinical outcomes including osteoarthritis progression and surgical results.