Artificial Intelligence-Based Portable Bioelectronics Platform for SARS-CoV-2 Diagnosis with Multi-nucleotide Probe Assay for Clinical Decisions

Tripathy, Suryasnata and Supraja, Patta and Mohanty, Swati and Sai, Vallepu Mohan and Agrawal, Tushant and Ch, Gajendranath Chaudhury and Singh, Shiv Govind and et al, . (2021) Artificial Intelligence-Based Portable Bioelectronics Platform for SARS-CoV-2 Diagnosis with Multi-nucleotide Probe Assay for Clinical Decisions. Analytical Chemistry, 93 (45). pp. 14955-14965. ISSN 0003-2700

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Abstract

In the context of the recent pandemic, the necessity of inexpensive and easily accessible rapid-test kits is well understood and need not be stressed further. In light of this, we report a multi-nucleotide probe-based diagnosis of SARS-CoV-2 using a bioelectronics platform, comprising low-cost chemiresistive biochips, a portable electronic readout, and an Android application for data acquisition with machine-learning-based decision making. The platform performs the desired diagnosis from standard nasopharyngeal and/or oral swabs (both on extracted and non-extracted RNA samples) without amplifying the viral load. Being a reverse transcription polymerase chain reaction-free hybridization assay, the proposed approach offers inexpensive, fast (time-to-result: ≤ 30 min), and early diagnosis, as opposed to most of the existing SARS-CoV-2 diagnosis protocols recommended by the WHO. For the extracted RNA samples, the assay accounts for 87 and 95.2% test accuracies, using a heuristic approach and a machine-learning-based classification method, respectively. In case of the non-extracted RNA samples, 95.6% decision accuracy is achieved using the heuristic approach, with the machine-learning-based best-fit model producing 100% accuracy. Furthermore, the availability of the handheld readout and the Android application-based simple user interface facilitates easy accessibility and portable applications. Besides, by eliminating viral RNA extraction from samples as a pre-requisite for specific detection, the proposed approach presents itself as an ideal candidate for point-of-care SARS-CoV-2 diagnosis. © 2021 American Chemical Society.

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IITH Creators:

IITH Creators	ORCiD
Ch, Gajendranath Chaudhury	https://orcid.org/0000-0002-5880-4023
Singh, Shiv Govind	http://orcid.org/0000-0001-7319-879X

Item Type:

Article

Uncontrolled Keywords:

Android applications; Clinical decision; Decisions makings; Electronic readout; Heuristics approaches; Low-costs; Portable electronics; Probe-based; Rapid test; Test kits

Subjects:

Electrical Engineering

Divisions:

Department of Electrical Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

05 Aug 2022 08:54

Last Modified:

05 Aug 2022 08:54

URI:

http://raiithold.iith.ac.in/id/eprint/10103