Biosensors and machine learning for enhanced detection, stratification, and classification of cells: a review

• Published in: Biomedical microdevices Vol. 24; no. 3; p. 26
• Main Authors: Raji, Hassan, Tayyab, Muhammad, Sui, Jianye, Mahmoodi, Seyed Reza, Javanmard, Mehdi
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-09-2022; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial intelligence; Biological and Medical Physics; Biomarkers; Biomedical Engineering and Bioengineering; Biophysics; Biosensors; Cell differentiation; Classification; Datasets; Deep learning; Disease; Engineering; Engineering Fluid Dynamics; Learning algorithms; Machine learning; Miniaturization; Nanotechnology; Sensors; Signal transduction; Support vector machines; Deep learning; Neural Networks; Machine Learning (ML); Support Vector Machine; Biosensors; Microfluidics
• ISSN: 1387-2176; 1572-8781
• DOI: 10.1007/s10544-022-00627-x

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 the 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.