基于特征融合深度学习模型的西中国青少年手和手腕的X射线骨年龄评估方法
Ya-Hui Wang1,2,3, Hui-Ming Zhou3,4, Lei Wan3,5
1College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, PR China.
International journal of legal medicine
|May 22, 2025
概括
一个新的深度学习模型使用手部X射线准确评估青少年的骨龄. 这种先进的骨成熟度评估工具显示出高精度,特别是在法定年龄的确定.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 儿科内分泌学 儿科内分泌学
背景情况:
- 对于临床和法律目的来说,青少年骨成熟度的评估至关重要.
- 传统的骨年龄 (BA) 评估依赖于手和手腕X射线的手动解释.
- 准确的BA分类具有挑战性,特别是在关键的法定年龄门周围.
研究的目的:
- 开发和评估深度学习 (DL) 模型,用于自动化骨年龄评估.
- 为了比较不同DL网络架构在BA分类中的性能.
- 评估DL模型的准确性与手动评估以及不同年龄组的准确性.
主要方法:
- 追溯分析688名青少年手和手腕X射线图像 (年龄为11.00-23.99岁).
- 四个DL模型的开发和比较:InceptionV3,InceptionV3+SE+Sex,InceptionV3+双线,以及InceptionV3+双线+SE+Sex.
- 对手册BA分类和四个年龄层对表现最好的DL模型的评估.
主要成果:
- 发明V3+双线+SE+性别DL模型实现了最高的分类准确性 (96.15%的培训,90.48%的测试组).
- 该模型在分类年龄为16.0-18.0岁和18.0岁以上的青少年方面显示出100%的准确性.
- 基于DL的BA分类显示了法律年龄决定的显著参考值.
结论:
- 功能融合深度学习模型为骨年龄评估提供了高度准确和自动化的方法.
- 开发的DL模型对于确定法定年龄特别有价值,特别是在关键值 (14.0,16.0,18.0岁).
- 这种人工智能驱动的方法可以提高青少年骨成熟度评估的效率和可靠性.
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