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中国汉族青少年和儿童肘部关节X射线的骨年龄估计基于多个深卷积神经网络模型
Dan-Yang Li1,2,3, Hui-Ming Zhou3,4, Lei Wan3
1Academy of Medical Sciences, Shanxi Medical University, Taiyuan 030000, China.
Fa yi xue za zhi
|May 21, 2025
概括
深度学习使用肘部X射线准确估计了中国儿童的骨龄. 使用与UNet++和DenseNet121模型的完整联合细分可以显著提高估计准确性.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 儿科内分泌学 儿科内分泌学
背景情况:
- 准确的骨龄估计对于评估儿童骨成熟度至关重要.
- 传统的方法可能是主观和耗时的.
- 深度学习为自动化和客观的骨年龄评估提供了潜力.
研究的目的:
- 开发和评估一个深度学习模型,从肘部X射线图像中自动估计骨龄.
- 为了比较不同的细分策略来优化模型性能.
- 在中国儿科人口中评估模型的准确性.
主要方法:
- 利用了943张6至16岁的汉族儿童的肘部X射线图像.
- 我们比较了三个方案:直接输入,用关键骨标注进行细分,用全骨标注进行细分.
- 使用U-Net,UNet++,TransUNet进行细分和各种CNN (VGG,Inception,ResNet,DenseNet) 进行回归.
- 在内部 (189张图像) 和外部 (104张图像) 测试套件上验证了模型.
主要成果:
- UNet++实现了高细分精度 (93.8%),并提供完整的联合注释 (图3).
- 采用方案3的DenseNet121模型在内部测试集中表现最好 (MAE:0.83年).
- 在外部测试组中,使用方案3的DenseNet121实现了0.89年的MAE和1.00年的RMSE.
结论:
- 使用UNet++进行细分和DenseNet121进行回归的深度学习方法对骨年龄的估计有效.
- 细分整个肘部关节 (遥远的膀臂,近距离半径,近距离肘部) 可以提高准确性.
- 这种自动化方法在临床实践中对客观骨年龄评估有希望.
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