Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia

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 (RHH...

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Autores principales:	Valeria Rizzuto, Arianna Mencattini, Begoña Álvarez-González, Davide Di Giuseppe, Eugenio Martinelli, David Beneitez-Pastor, Maria del Mar Mañú-Pereira, Maria José Lopez-Martinez, Josep Samitier
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ef93268b7f6d46169f3c3acc7cd6ac84
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spelling	oai:doaj.org-article:ef93268b7f6d46169f3c3acc7cd6ac842021-12-02T14:34:02ZCombining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia10.1038/s41598-021-92747-22045-2322https://doaj.org/article/ef93268b7f6d46169f3c3acc7cd6ac842021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92747-2https://doaj.org/toc/2045-2322Abstract 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 restore 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%.Valeria RizzutoArianna MencattiniBegoña Álvarez-GonzálezDavide Di GiuseppeEugenio MartinelliDavid Beneitez-PastorMaria del Mar Mañú-PereiraMaria José Lopez-MartinezJosep SamitierNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Valeria Rizzuto Arianna Mencattini Begoña Álvarez-González Davide Di Giuseppe Eugenio Martinelli David Beneitez-Pastor Maria del Mar Mañú-Pereira Maria José Lopez-Martinez Josep Samitier Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia
description	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 restore 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%.
format	article
author	Valeria Rizzuto Arianna Mencattini Begoña Álvarez-González Davide Di Giuseppe Eugenio Martinelli David Beneitez-Pastor Maria del Mar Mañú-Pereira Maria José Lopez-Martinez Josep Samitier
author_facet	Valeria Rizzuto Arianna Mencattini Begoña Álvarez-González Davide Di Giuseppe Eugenio Martinelli David Beneitez-Pastor Maria del Mar Mañú-Pereira Maria José Lopez-Martinez Josep Samitier
author_sort	Valeria Rizzuto
title	Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia
title_short	Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia
title_full	Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia
title_fullStr	Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia
title_full_unstemmed	Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia
title_sort	combining microfluidics with machine learning algorithms for rbc classification in rare hereditary hemolytic anemia
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ef93268b7f6d46169f3c3acc7cd6ac84
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Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia

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