使用深度学习和拉曼光谱对骨关节炎和健康软骨进行分类
Yong En Kok1, Anna Crisford2, Andrew Parkes3
1School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK. yong.kok@nottingham.ac.uk.
Scientific reports
|July 10, 2024
概括
一个新的卷积神经网络 (CNN) 自动化了用于软骨分析的拉曼光谱预处理. 这种人工智能方法在分类骨关节炎和骨质疏松症方面实现了高准确性,有助于临床诊断.
科学领域:
- 生物医学工程 生物医学工程
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 拉曼光谱技术提供了对生物组织的快速分子分析.
- 频谱数据噪声需要广泛的预处理才能进行准确的分析.
- 自动化预处理可以提高拉曼光谱的临床效用.
研究的目的:
- 开发一个端到端的卷积神经网络 (CNN),用于自动化拉曼光谱预处理和分析.
- 在骨关节炎和骨质疏松症患者中从表面和深层软骨层分类拉曼光谱.
- 使用集成梯度识别生物相关的光谱特征.
主要方法:
- 一个端到端的多卷积神经网络 (M-CNN) 旨在学习最佳的预处理策略.
- 在M-CNN中,从45名骨关节炎和19名骨质疏松症患者的软骨拉曼光谱上进行了6倍交叉验证,进行了训练和验证.
- 集成梯度被用来识别有助于网络决策的关键拉曼光谱特征.
主要成果:
- 在原始或手工预处理的光谱上,M-CNN的分类准确度与传统的CNN相美或更高.
- 确认的拉曼签名 (特征) 已被证实具有生物相关性.
- 使用人工神经网络,决策树和支持矢量机器的特征选择表明,最小的特征 (疾病分类<3,层分配<300) 产生了可比的性能.
结论:
- 拟议的AI方法自动化了拉曼光谱的复杂预处理和特征选择.
- 这种方法显示了促进基于拉曼光谱的诊断的临床翻译的潜力.
- 该技术减少了在光谱分析中劳累的手工预处理和特征选择的需要.
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