Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia
Rizzuto, Valeria (Universitat de Barcelona. Departament de Medicina)
Mencattini, Arianna (University of Rome Tor Vergata)
Álvarez-González, Begoña (Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina)
Di Giuseppe, Davide (University of Rome Tor Vergata)
Beneitez Pastor, David (Hospital Universitari Vall d'Hebron)
Mañú Pereira, María del Mar (Hospital Universitari Vall d'Hebron)
Lopez-Martinez, Maria José (Universitat de Barcelona. Departament d'Enginyeria Electrònica i Biomèdica)
Samitier, Josep (Universitat de Barcelona. Departament d'Enginyeria Electrònica i Biomèdica)
Institut Germans Trias i Pujol. Institut de Recerca contra la Leucèmia Josep Carreras

Date:	2021
Abstract:	Combining microfluidics technology with machine learning represents an innovative approach to conduct massive quantitative cell behavior study and implement smart decision-making systems in support of clinical diagnostics. The spleen plays a key-role in rare hereditary hemolytic anemia (RHHA), being the organ responsible for the premature removal of defective red blood cells (RBCs). The goal is to adapt the physiological spleen filtering strategy for in vitro study and monitoring of blood diseases through RBCs shape analysis. Then, a microfluidic device mimicking the slits of the spleen red pulp area and video data analysis are combined for the characterization of RBCs in RHHA. This microfluidic unit is designed to evaluate RBC deformability by maintaining them fixed in planar orientation, allowing the visual inspection of RBC's capacity to restre their original shape after crossing microconstrictions. Then, two cooperative learning approaches are used for the analysis: the majority voting scheme, in which the most voted label for all the cell images is the class assigned to the entire video; and the maximum sum of scores to decide the maximally scored class to assign. The proposed platform shows the capability to discriminate healthy controls and patients with an average efficiency of 91%, but also to distinguish between RHHA subtypes, with an efficiency of 82%.
Grants:	Agència de Gestió d'Ajuts Universitaris i de Recerca 2017SGR1079 European Commission. Horizon 2020 860436 Ministerio de Economía, Industria y Competitividad RD16/0006/0012
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Anemia, Hemolytic, Congenital ; Erythrocyte Deformability ; Erythrocytes ; Female ; Humans ; Image Processing, Computer-Assisted ; Lab-On-A-Chip Devices ; Machine Learning ; Male ; Microfluidic Analytical Techniques
Published in:	Scientific reports, Vol. 11 Núm. 1 (december 2021) , p. 13553, ISSN 2045-2322

DOI: 10.1038/s41598-021-92747-2
PMID: 34193899

12 p, 3.6 MB

The record appears in these collections:
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Institut d'Investigació en Ciencies de la Salut Germans Trias i Pujol (IGTP)
Articles > Research articles
Articles > Published articles