Classification of chemically modified red blood cells in microflow using machine learning video analysis

Baskaran, R.K. R., Link, A. , Porr, B. and Franke, T. (2024) Classification of chemically modified red blood cells in microflow using machine learning video analysis. Soft Matter, 20(5), pp. 952-958. (doi: 10.1039/D3SM01337E) (PMID:38088860)

Abstract

We classify native and chemically modified red blood cells with an AI based video classifier. Using TensorFlow video analysis enables us to capture not only the morphology of the cell but also the trajectories of motion of individual red blood cells and their dynamics. We chemically modify cells in three different ways to model different pathological conditions and obtain classification accuracies for all three classification tasks of more than 90% between native and modified cells. Unlike standard cytometers that are based on immunophenotyping our microfluidic cytometer allows to rapidly categorize cells without any fluorescence labels simply by analysing the shape and flow of red blood cells.

• Item Type: Articles
• Additional Information: The works was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813786 (EVOdrops). Additionally, the authors acknowledge support from the UK Engineering and Physical Sciences Research Council (EPSRC) via grant EP/P018882/1.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Porr, Dr Bernd and Franke, Professor Thomas and Link, Mr Andreas
• Authors: Baskaran, R.K. R., Link, A., Porr, B., and Franke, T.
• College/School: College of Science and Engineering > School of Engineering > Biomedical Engineering
• Journal Name: Soft Matter
• Publisher: Royal Society of Chemistry
• ISSN: 1744-683X
• ISSN (Online): 1744-6848
• Published Online: 04 December 2023
• Copyright Holders: Copyright: © The Royal Society of Chemistry 2023
• First Published: First published in Soft Matter 20:952-958
• Publisher Policy: Reproduced under a Creative Commons licence

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
174078		Thin Film Platform Technologies for Conformable and Mechanically Flexible Biosensors	Thomas Franke	Engineering and Physical Sciences Research Council (EPSRC)	EP/P018882/1	ENG - Biomedical Engineering