COVID-19 Detection Using Contemporary Biosensors and Machine Learning Approach: A Review

IF 3.7 4区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Sajal Agarwal;Rupam Srivastava;Santosh Kumar;Yogendra Kumar Prajapati
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引用次数: 0

Abstract

The current global pandemic not only claims countless human lives but also rocks the economies of every country on the planet. This fact needs the development of novel, productive, and efficient techniques to detect the SARS-CoV-2 virus. This review article discusses the current state of SARS-CoV-2 virus detection methods such as electrochemical, fluorescent, and electronic, etc., as well as the potential of optical sensors with a wide range of novel approaches and models. This review provides a comprehensive comparison of various detection methods by comparing the various techniques in depth. In addition, there is a brief discussion of the futuristic approach combining optical sensors with machine learning algorithms. It is believed that this study would prove to be critical for the scientific community to explore solutions for detecting viruses with improved functionality.
使用当代生物传感器和机器学习方法检测 COVID-19:综述。
当前的全球大流行病不仅夺去了无数人的生命,而且还动摇了地球上每个国家的经济。这一事实需要开发新型、高效的 SARS-CoV-2 病毒检测技术。这篇综述文章讨论了电化学、荧光和电子等 SARS-CoV-2 病毒检测方法的现状,以及光学传感器的潜力和各种新型方法和模型。本综述通过深入比较各种技术,对各种检测方法进行了全面比较。此外,还简要讨论了将光学传感器与机器学习算法相结合的未来方法。我们相信,这项研究对于科学界探索具有更多功能的病毒检测解决方案至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on NanoBioscience
IEEE Transactions on NanoBioscience 工程技术-纳米科技
CiteScore
7.00
自引率
5.10%
发文量
197
审稿时长
>12 weeks
期刊介绍: The IEEE Transactions on NanoBioscience reports on original, innovative and interdisciplinary work on all aspects of molecular systems, cellular systems, and tissues (including molecular electronics). Topics covered in the journal focus on a broad spectrum of aspects, both on foundations and on applications. Specifically, methods and techniques, experimental aspects, design and implementation, instrumentation and laboratory equipment, clinical aspects, hardware and software data acquisition and analysis and computer based modelling are covered (based on traditional or high performance computing - parallel computers or computer networks).
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