一个基于InceptionV3的新型双输出深度学习模型,用于放射性骨年龄和性别评估
Baraa Rayed1, Hakan Amasya2,3,4, Mana Sezdi5,4
1Biomedical Engineering Department, Institute of Graduate Studies, Istanbul University-Cerrahpasa, 34320, Istanbul, Turkey. baraarayed@ogr.iuc.edu.tr.
Journal of imaging informatics in medicine
|August 4, 2025
概括
这项研究引入了一种计算机辅助系统,用于从手腕X射线图中预测骨年龄和性别. 人工智能模型显示出有希望的结果,尽管高硬件要求可能会限制临床使用.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 手和手腕的X线图是骨年龄评估的标准.
- 传统的方法有其局限性.
- 计算机辅助系统可以提高准确性和效率.
研究的目的:
- 开发一个多输出预测模型的骨龄和性别使用数字射线图.
- 为了提高诊断能力,利用深度学习.
主要方法:
- 使用了InceptionV3架构与专门的卷积神经网络层 (例如,Squeeze-and-Excitation块).
- 在2017年RSNA儿科骨年龄挑战数据集 (14,048个样本) 中接受培训和测试.
- 在培训,验证和测试数据分割方面采用了7:2:1的比例.
主要成果:
- 实现了骨年龄预测的平均绝对误差为3.1.
- 在性别分类方面达到95%的准确性和97%的AUC.
- 骨年龄的高类内相关性 (0.997) 和性别的科恩卡帕 (0.898).
结论:
- 拟议的AI模型在从X射线图中预测骨年龄和性别方面表现出高准确性.
- 模型识别共同和离散特征的能力提高了效率.
- 未来的工作应该集中在数据集扩展和算法简化上,以实现更广泛的临床采用.
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