Biosensors and Machine Learning for Enhanced Detection, Stratification, and Classification of Cells: A Review

TL;DR

This review discusses how biosensors combined with machine learning techniques improve the detection, classification, and stratification of cells, advancing point-of-care diagnostics and personalized medicine.

Contribution

It provides a comprehensive overview of the application of machine learning to various cell-detecting biosensors and compares their effectiveness and data requirements.

Findings

Machine learning enhances cell classification accuracy.

Different sensing modalities vary in effectiveness.

Dataset size impacts classifier performance.

Abstract

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.