Biosensors and Machine Learning for Enhanced Detection, Stratification, and Classification of Cells: A Review
This review is significant for researchers and developers in disease diagnostics and therapeutics, providing an overview of enhanced cell analysis through biosensors and machine learning.
This review paper examines the application of machine learning to biosensors for cell detection, stratification, and classification. It compares how different sensing modalities and algorithms influence classifier accuracy and the necessary dataset size.
Biological cells, by definition, are the basic units which contain the fundamental molecules of life of which all living things are composed. Understanding how they function and differentiating cells from one another therefore 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 with each passing day. Furthermore, Machine Learning has allowed for enhancement in analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories using a data-driven approach rather than physics-driven. In this review, we provide an account of how Machine Learning has been 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.