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可穿戴的智能出汗平台用于SERS-AI痛风诊断

Zhaoxian Chen¹, Wei Wang¹, Hao Tian¹

¹Chongqing Key Laboratory of Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing, 400044, China. zhengxueli0315@cqu.edu.cn.

|February 19, 2024

概括

这项研究介绍了一个可穿戴的汗水分析平台,使用表面增强的拉曼光谱 (SERS) 和人工智能 (AI) 进行痛风诊断. 智能系统准确地检测出汗中的尿酸 (UA),提供一种非侵入性的健康监测解决方案.

科学领域:

生物医学工程生物医学工程
分析化学分析化学
可穿戴技术可穿戴技术

背景情况:

用于健康监测的汗水分析正在通过可穿戴技术获得引力.
使用表面增强拉曼光谱 (SERS) 进行痛风诊断的复杂汗水矩阵中敏感检测尿酸 (UA) 仍然具有挑战性.

研究的目的:

开发一个可视化和智能化的可穿戴汗水平台,用于UA的SERS检测.
通过对来自汗水的SERS数据的AI驱动分析来实现精确的痛风诊断.

主要方法:

一个可穿戴的平台,配有带有色度纸和银纳米线 (AgNWs) 的螺旋通道,用于吸收汗水和SERS测量.
基于智能手机的图像识别,用于智能定量汗水pH值和体积.
使用人工智能 (AI) 算法,特别是人工神经网络 (ANN) 来分析SERS光谱,用于痛风识别.

主要成果:

开发的可穿戴平台成功地在人类汗水中进行了SERS检测UA.
在基于SERS光谱的基础上,ANN算法在识别痛风方面取得了很高的准确性 (97%).
使用智能手机图像识别实现了汗水pH和体积的量化.

结论:

集成的SERS-AI可穿戴汗水平台是进行非侵入性痛风诊断的可行策略.
这种方法通过汗水分析扩大了舒适和持续健康监测的可能性.
智能平台展示了个性化健康管理中更广泛应用的潜力.

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