Chapter 16 - Biosensors for healthcare: an artificial intelligence approach

Abstract

Biological cells, by definition, are the basic units that contain the fundamental molecules of life of which all living things are composed. Therefore, understanding how they function and differentiating cells from one another, is of paramount importance for disease diagnostics as well as therapeutics. Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to point-of-care (POC) solutions. Biosensor-based devices are revolutionary in contemporary biomedical applications and will be the future of health care. The coupling of artificial intelligence (AI) for biosensors for POC diagnostics is a prominent upcoming field. AI is changing the world we live in, and it has the potential to transform struggling healthcare systems with new efficiencies, new therapies, new diagnostics, and new economies. Already, AI is having an impact on healthcare, and new prospects for greater advancements happen daily. Furthermore, machine learning (ML) has allowed for enhancements in the analytical capabilities of these various biosensing modalities, especially the challenging task of classifying cells into various categories using a data-driven approach rather than a physics-driven one. In this review, we provide an account of how ML is applied explicitly to sensors that detect and classify cells. We also provide a comparison of how different sensing modalities and algorithms affect the classifier accuracy and the dataset size required. This chapter describes key ML algorithms for innovative biosensors and microchip-based essential biomarkers for the Internet of Things, computational technology, and real-time health monitoring for patient convenience.