Advancements in SARS-CoV-2 Testing: Enhancing Accessibility through Machine Learning-Enhanced Biosensors

Abstract

The COVID-19 pandemic highlighted the importance of widespread testing for SARS-CoV-2, leading to the development of various new testing methods. However, traditional invasive sampling methods can be uncomfortable and even painful, creating barriers to testing accessibility. In this article, we explore how machine learning-enhanced biosensors can enable non-invasive sampling for SARS-CoV-2 testing, revolutionizing the way we detect and monitor the virus. By detecting and measuring specific biomarkers in body fluids or other samples, these biosensors can provide accurate and accessible testing options that do not require invasive procedures. We provide examples of how these biosensors can be used for non-invasive SARS-CoV-2 testing, such as saliva-based testing. We also discuss the potential impact of non-invasive testing on accessibility and accuracy of testing. Finally, we discuss potential limitations or biases associated with the machine learning algorithms used to improve the biosensors and explore future directions in the field of machine learning-enhanced biosensors for SARS-CoV-2 testing, considering their potential impact on global healthcare and disease control.

Keywords: COVID-19; artificial intelligence; biosensors; contrastive learning; deep learning; machine learning; sensors.

Figure 1

Non-invasive biosensors are enhanced with ML algorithms for medical applications.

Figure 2

Schematic illustration of ML algorithms; (a) SVM; (b) random forest; (c) PCA-ICA; and (d) RRN.

Figure 3

Machine learning algorithms improve biosensor signal by distinguishing it from measurement noise.

Figure 4

Contrastive learning highlights distinctions between various signals.