Scholarlink

Objectives <p>This study developed a magnetic resonance imaging (MRI)-based artificial intelligence (AI) framework for ordered pubertal olecranon bone age (BA) staging and evaluated internal performance and preliminary cross-centre transportability.</p> Materials and methods <p>This retrospective two-centre study included a development cohort of 197 elbow MRI examinations from Shengjing Hospital of China Medical University and an independent external-validation cohort of 72 elbow MRI examinations from Handan Central Hospital. The development cohort comprised 77 girls aged 9.5–13 years and 120 boys aged 11.5–15 years, whereas the external-validation cohort comprised 30 girls aged 9.5–13 years and 42 boys aged 11.5–15 years. Manually delineated olecranon ROIs were used for preprocessing and YOLO-v8m detector training. Localized images were processed using ResNet-50 feature extraction, Fisher score-based feature selection, and an NN classifier. The locked development-centre pipeline was applied unchanged to the external-validation cohort without retraining, repeated feature selection, hyperparameter tuning, or checkpoint selection based on external data.</p> Results <p>Automated localization achieved a mean average precision of 1.0. The final NN classifier reached internal-validation accuracies of 94.12% in girls and 93.75% in boys, with stage-mapped mean absolute error (MAE)/root mean square error (RMSE) values of 0.029/0.121 and 0.062/0.250 years, respectively. In the external cohort, girls achieved an accuracy of 86.67%, MAE of 0.233 years, and RMSE of 0.658 years, weighted kappa of 0.922, and ICC of 0.969, whereas boys achieved an accuracy of 90.48%, MAE of 0.095 years, RMSE of 0.309 years, unweighted kappa of 0.890, weighted kappa of 0.963, and ICC of 0.991.</p> Conclusion <p>The MRI-based framework provided stage-consistent internal performance and preliminary evidence of cross-centre transportability within the studied pubertal age window. It should be interpreted as a supplementary maturity marker rather than a stand-alone forensic age-estimation tool.</p> Trial registration <p>Chinese Clinical Trial Registry (ChiCTR2500109735), retrospectively registered on September 24, 2025.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Automated MRI-based framework combining deep learning localization and machine learning classification for pubertal olecranon bone age assessment

  • 期刊论文

  • 发表日期：

    2026年7月28日

查看全文
  • Yuting Luo,
  • Xiaofei Zhang,
  • Shinong Pan,
  • Fuyao Yu

摘要

Objectives

This study developed a magnetic resonance imaging (MRI)-based artificial intelligence (AI) framework for ordered pubertal olecranon bone age (BA) staging and evaluated internal performance and preliminary cross-centre transportability.

Materials and methods

This retrospective two-centre study included a development cohort of 197 elbow MRI examinations from Shengjing Hospital of China Medical University and an independent external-validation cohort of 72 elbow MRI examinations from Handan Central Hospital. The development cohort comprised 77 girls aged 9.5–13 years and 120 boys aged 11.5–15 years, whereas the external-validation cohort comprised 30 girls aged 9.5–13 years and 42 boys aged 11.5–15 years. Manually delineated olecranon ROIs were used for preprocessing and YOLO-v8m detector training. Localized images were processed using ResNet-50 feature extraction, Fisher score-based feature selection, and an NN classifier. The locked development-centre pipeline was applied unchanged to the external-validation cohort without retraining, repeated feature selection, hyperparameter tuning, or checkpoint selection based on external data.

Results

Automated localization achieved a mean average precision of 1.0. The final NN classifier reached internal-validation accuracies of 94.12% in girls and 93.75% in boys, with stage-mapped mean absolute error (MAE)/root mean square error (RMSE) values of 0.029/0.121 and 0.062/0.250 years, respectively. In the external cohort, girls achieved an accuracy of 86.67%, MAE of 0.233 years, and RMSE of 0.658 years, weighted kappa of 0.922, and ICC of 0.969, whereas boys achieved an accuracy of 90.48%, MAE of 0.095 years, RMSE of 0.309 years, unweighted kappa of 0.890, weighted kappa of 0.963, and ICC of 0.991.

Conclusion

The MRI-based framework provided stage-consistent internal performance and preliminary evidence of cross-centre transportability within the studied pubertal age window. It should be interpreted as a supplementary maturity marker rather than a stand-alone forensic age-estimation tool.

Trial registration

Chinese Clinical Trial Registry (ChiCTR2500109735), retrospectively registered on September 24, 2025.