使用拉曼光谱和可解释机器学习对健康的ex vivo牛组织进行区分
Soha Yousuf1, Mohamed Irfan Karukappadath1, Azhar Zam1,2
1Division of Engineering, Laboratory for Advanced Bio-Photonics and Imaging (LAB-π), New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Lasers in surgery and medicine
|May 28, 2025
概括
可解释的机器学习模型与拉曼光谱学 (RS) 结合,可以准确地区分骨科组织. 识别关键的拉曼生物标志物提高了对RS进行手术指导的透明度和信任.
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.
- 机器学习 机器学习
背景情况:
- 拉曼光谱 (RS) 显示了在骨科手术中的手术内指导的前景.
- 机器学习 (ML) 中有限的算法透明度阻碍了临床医生的信任.
研究的目的:
- 开发可解释的ML模型来分类牛组织 (骨,骨髓,脂肪,肌肉).
- 确定关键的拉曼生物标志物,以提高模型透明度和临床信任.
主要方法:
- 使用便携式RS系统从牛组织中收集光谱数据.
- 开发和评估1D-CNN和SVM模型用于组织分类.
- 生成特征重要性地图和匹配分数以识别拉曼生物标志物.
主要成果:
- 确定了关键的拉曼生物标志物:酸,脂质,氨基酸和原.
- 这些生物标志物对于区分不同类型的牛组织至关重要.
- 提供了关于组织之间的分子差异的见解.
结论:
- 与RS集成的可解释的ML模型准确地区分了骨科组织.
- 生物标记物识别提高了透明度,支持RS作为精确手术的可靠工具.
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