Detection of SARS-CoV-2 (COVID-19) by SERS Spectroscopy Combined With Artificial Intelligence

Overview

SARS-CoV-2 infection was identified as responsible for several cases of pneumonia and acute respiratory distress syndromes described in Wuhan, Hubei Province, China in December 2019. A global epidemic has spread since and the Director General of the World Health Organization (WHO) declared in March 2020 the state of a global pandemic. As the spread of the virus accelerates, several countries are implementing containment strategies to stem the epidemic. The context of an influx of patients and congestion in healthcare establishments requires rapid and reliable diagnostic solutions for SARS-CoV-2 infection in order to enable patients to be properly referred. These solutions will represent fundamental tools in the management of new epidemic waves, both in terms of health and economics.

Spectroscopy is the discipline of studying the interactions between light and matter, in order to perform analyzes unmatched in terms of the speed of data acquisition. Depending on the spectral ranges used by the sensors, it is possible to carry out molecular (molecular and vibrational spectroscopy) or elementary (atomic spectroscopy) analyzes. As part of this project, GreenTropism has selected Surface Enhanced Raman Scattering (SERS) technology as a spectral technique. The scientific literature reports several cases of use of SERS technology for virus analysis, under variable conditions: variable viral loads, after amplification, use of substrates enriched in antigens. The SERS allows an analysis of a sample deposited on a substrate on average (from fifteen seconds to 10 minutes depending on the devices and the presence of complementary imaging). Already proven for the identification of viruses on strains pathogenic for humans and animals, its deployment is slowed down by the complexity of the data to be processed. These spectra acquisition technologies require the joint use of statistical tools and multivariate analyzes to allow sample discrimination (classification) and / or quantification. Until recently, the capacity and performance of statistical tools were limited by the available computational capacities. The lifting of this technological lock allowed the advent and democratization of Artificial Intelligence (AI) techniques theorized in the 1960s and applied today. GreenTropism's Kaïssa, AI tool, in addition to processing big data, has been designed and trained specifically for processing spectral data and automating all of the algorithmic chains needed to go from spectrum support to its interpretation, and the presentation of the final answer. The analysis of chemometric data, for the purpose of classification, implements several types of algorithms that Kaïssa uses, combining them automatically to obtain the best possible analyzes of these data. These algorithms are divided into two large groups: mathematical preprocessing and classification models. The combination of photonic technologies (here SERS) and AI allows real-time analyzes of multiple substrates, without a priori knowledge of the user on the sample and without prior expertise. These characteristics make it a valuable tool for diagnosing SARS-CoV-2 infection in the context of Point Of Care. In a work carried out between the months of March and June 2020, several models showed, on test databases not integrated in the learning, performance of discrimination between positive and negative patients for SARS-CoV-2 according to RT-PCR equivalent to Youden indices of 0.6 to 0.92. On the other hand, these models have highlighted a variability in the use of samples which results in a drop in performance during tests on statistically independent databases requiring additional spectral acquisitions, leading today to the presentation of this report project.