用指纹指导的汗道之间的相关性来自OCT和糖尿病神经病变的特征使用沃罗诺伊图
Wangbiao Li1, Zhida Chen1, Hui Lin1
1Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, China.
Journal of biophotonics
|July 19, 2025
概括
光学连贯断层扫描可以评估指纹汗道,以确定糖尿病神经病变 (DN). 汗道特征与DN严重程度相关,为糖尿病患者的早期检测提供潜在的非侵入性生物标志物.
科学领域:
- 生物医学工程 生物医学工程
- 眼科医生 眼科 眼科
- 神经学 神经学
背景情况:
- 糖尿病神经病变 (DN) 是一种常见的糖尿病并发症,影响交感神经.
- 神经病影响皮肤的热调节和汗道形态.
- 目前用于DN的诊断方法可能是侵入性的或缺乏灵敏度.
研究的目的:
- 调查汗道特征与DN严重程度之间的相关性.
- 使用光学连贯断层扫描 (OCT) 和机器学习开发DN的预测模型.
- 探索海外国家和地区评估的汗道特征作为DN的非侵入性生物标志物.
主要方法:
- 用指纹指导的汗道被使用OCT评估.
- 主要组件分析 (PCA) 和反向传播神经网络 (BPNN) 用于预测建模.
- 使用沃罗诺伊图进行了空间分布分析.
主要成果:
- 排汗管的数量,体积和间距与DN严重程度有显著的相关性.
- 在DN患者中观察到汗道空间分布的不规则.
- 基于PCA的BPNN模型在不同DN阶段实现了良好的预测准确性.
结论:
- 经过OCT评估的汗道特征与DN严重程度相关.
- 开发的预测模型表明了准确DN分类的潜力.
- 汗道的OCT成像为DN生物标志物发现提供了一个有希望的非侵入性方法.
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