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机器学习增强的表面增强光谱技术助力下一代分子诊断

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics.

作者信息

Zhou Hong, Xu Liangge, Ren Zhihao, Zhu Jiaqi, Lee Chengkuo

机构信息

Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583

Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608.

出版信息

Nanoscale Adv. 2022 Nov 7;5(3):538-570. doi: 10.1039/d2na00608a. eCollection 2023 Jan 31.

DOI:10.1039/d2na00608a
PMID:36756499
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9890940/
Abstract

The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.

摘要

当今世界见证了分子检测和筛查在医疗保健及医学诊断中所发挥的重要作用以及巨大需求，尤其是在新冠疫情爆发期间。表面增强光谱技术，包括表面增强拉曼散射（SERS）和红外吸收（SEIRA），可提供与分子组成、化学键及构型直接相关的晶格和分子振动指纹信息。这些特性使其成为用于分子诊断和筛查的明确、无损且无需标记的工具包。然而，分子诊断中的新问题，如分子种类增加、病毒传播加快以及对检测准确性和灵敏度的更高要求，给检测技术带来了巨大挑战。人工智能和机器学习（ML）技术的进步在使SERS和SEIRA具备快速分析和自动数据处理能力以共同应对这一挑战方面显示出了巨大潜力。本综述通过研究ML算法如何有益于SERS/SEIRA、讨论ML与SEIRA/SERS结合的一般过程、突出基于ML结合SEIRA/SERS的分子诊断和筛查应用以及提供对ML集成SEIRA/SERS未来发展的展望，介绍了ML与SERS/SEIRA的结合。总体而言，本综述提供了有关ML集成SEIRA/SERS用于分子诊断和筛查的最新进展及未来前景的全面知识。

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