通过光学活检基于折射率传感的肝脏组织病理变化的分类
Kacper Cierpiak1, Sebastián García-Galán2, Jakub Czubek1
1Department of Optoelectronics, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, 11/12 Narutowicza Street, Gdansk, 80-233, Poland; Opto and Neurophotonics Laboratory, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland.
这项研究引入了一种用于光学活检的新型光纤传感器,使用折射率 (RI) 来分类肝脏组织. 机器学习显著提高了诊断准确度,以区分健康,类似HCC和转移性组织.
科学领域:
- 生物医学光学 生物医学光学
- 医学诊断 医学诊断 医学诊断
- 光纤传感传感器是指光纤传感器.
背景情况:
- 光学活检提供了最少的侵入性组织评估,但需要快速,客观的分析.
- 紧型传感器对于光学活检技术的临床转化至关重要.
研究的目的:
- 开发一个基于折射率 (RI) 驱动的光学活检分类框架,使用光纤干扰计.
- 通过机器学习实现肝脏组织的准确和可解释的诊断签名.
主要方法:
- 利用外部光纤的Fabry-Pérot干涉测量腔来测量生物相关RI范围 (1.33-1.42) 的反射光谱.
- 根据文献指导的RI窗口定义了三个代理类 (健康,HCC类,转移性).
- 设计了62个光谱描述符,并采用了机器学习模型 (树组合,SVM) 和一个模糊的专家系统进行分类.
主要成果:
- 仅使用物理的RI估计实现了0.70准确度和0.48宏F1.
- 机器学习模型显著提高了分类性能,树组合达到1.00的宏F1.
- 特征归属证实RI是主要的区分信号,光谱可见度指标提高了强度.
结论:
- 机器学习增强光纤干扰度为光学活检提供了准确和可解释的诊断签名.
- 开发的框架支持基于RI的光学活检用于肝脏组织分析的翻译潜力.
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