Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy

Szymborski, Tomasz R.; Berus, Sylwia M.; Nowicka, Ariadna B.; Słowiński, Grzegorz; Kamińska, Agnieszka

doi:10.3390/biomedicines12010167

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Biomedicines

2024

DOI: 10.3390/biomedicines12010167

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Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy

Tomasz R. Szymborski,

Sylwia M. Berus,

Ariadna B. Nowicka

et al.

Abstract: The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spe… Show more

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Cited by 2 publications

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AMIKOMNET: Novel Structure for a Deep Learning Model to Enhance COVID-19 Classification Task Performance

Hanafi

2024

BDCC

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Since early 2020, coronavirus has spread extensively throughout the globe. It was first detected in Wuhan, a province in China. Many researchers have proposed various models to solve problems related to COVID-19 detection. As traditional medical approaches take a lot of time to detect the virus and require specific laboratory tests, the adoption of artificial intelligence (AI), including machine learning, might play an important role in handling the problem. A great deal of research has seen the adoption of AI succeed in the early detection of COVID-19 using X-ray images. Unfortunately, the majority of deep learning adoption for COVID-19 detection has the shortcomings of high error detection and high computation costs. In this study, we employed a hybrid model using an auto-encoder (AE) and a convolutional neural network (CNN) (named AMIKOMNET) with a small number of layers and parameters. We implemented an ensemble learning mechanism in the AMIKOMNET model using Adaboost with the aim of reducing error detection in COVID-19 classification tasks. The experimental results for the binary class show that our model achieved high effectiveness, with 96.90% accuracy, 95.06% recall, 94.67% F1-score, and 96.03% precision. The experimental result for the multiclass achieved 95.13% accuracy, 94.93% recall, 95.75% F1-score, and 96.19% precision. The adoption of Adaboost in AMIKOMNET for the binary class increased the effectiveness of the model to 98.45% accuracy, 96.16% recall, 95.70% F1-score, and 96.87% precision. The adoption of Adaboost in AMIKOMNET in the multiclass classification task also saw an increase in performance, with an accuracy of 96.65%, a recall of 94.93%, an F1-score of 95.76%, and a precision of 96.19%. The implementation of AE to handle image feature extraction combined with a CNN used to handle dimensional image feature reduction achieved outstanding performance when compared to previous work using a deep learning platform. Exploiting Adaboost also increased the effectiveness of the AMIKOMNET model in detecting COVID-19.

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AMIKOMNET: Novel Structure for a Deep Learning Model to Enhance COVID-19 Classification Task Performance

Hanafi

2024

BDCC

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Molecular Understanding of the Surface-Enhanced Raman Spectroscopy Salivary Fingerprint in People after Sars-COV-2 Infection and in Vaccinated Subjects

Rodà,

Gualerzi,

Picciolini

et al. 2024

Chemosensors

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The rapid spread of SARS-COV-2 and the millions of worldwide deaths and hospitalizations have prompted an urgent need for the development of screening tests capable of rapidly and accurately detecting the virus, even in asymptomatic people. The easy collection and the biomarker content of saliva, together with the label-free and informative power of surface-enhanced Raman spectroscopy (SERS) analysis have driven the creation of point-of-care platforms capable of identifying people with COVID-19. Indeed, different salivary fingerprints were observed between uninfected and infected people. Hence, we performed a retrospective analysis of SERS spectra from salivary samples of COVID-19-infected and -vaccinated subjects to understand if viral components and/or the immune response are implicated in spectral variations. The high sensitivity of the proposed SERS-based method highlighted the persistence of molecular alterations in saliva up to one month after the first positive swab, even when the subject tested negative for the rapid antigenic test. Nevertheless, no specific spectral patterns attributable to some viral proteins and immunoglobulins involved in COVID-19 infection and its progression were found, even if differences in peak intensity, presence, and position were observed in the salivary SERS fingerprint.

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Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy

Cited by 2 publications

References 60 publications

AMIKOMNET: Novel Structure for a Deep Learning Model to Enhance COVID-19 Classification Task Performance

AMIKOMNET: Novel Structure for a Deep Learning Model to Enhance COVID-19 Classification Task Performance

Molecular Understanding of the Surface-Enhanced Raman Spectroscopy Salivary Fingerprint in People after Sars-COV-2 Infection and in Vaccinated Subjects

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