Machine learning microfluidic based platform - Integration of Lab-on-Chip devices and data analysis algorithms for red blood cell plasticity evaluation in Pyruvate Kinase Disease monitoring

Aug 6, 2026

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Microfluidics enables large-scale biological experiments, but the volume of data often limits quantitative and reliable conclusions. This study proposes integrating machine learning with lab-on-chip devices to strengthen diagnostic performance, embedding data analysis within deep learning to encode cell morphology beyond standard appearance. A machine-learning microfluidics platform is used to test murine RBC samples in-flow, measuring pyruvate kinase disease plasticity defects via deformation recognition during pillar navigation. Results exceed 85% accuracy for distinguishing PKD from controls in both simulated and real experiments.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Microfluidic and Lab-on-Chip technologies

Machine learning microfluidics (MLM)

Platform capabilities and high-throughput analysis

Article overview and objective

Sensors & Actuators: A. Physical 351 (2023) 114187 Contents lists available at ScienceDirect Sensors and Actuators: A. Physical journal [homepage:](homepage: www.journals.elsevier.com/sensors-and-actuators-a-physical)[ www.journals.elsevier.com/sensors-and-actuators-a-physical](homepage: www.journals.elsevier.com/sensors-and-actuators-a-physical) | Machine learning microfluidic based platform: Integration of Lab-on-Chip devices and data analysis algorithms for red blood cell plasticity evaluation in Pyruvate Kinase Disease monitoring
A. Mencattini a, b, V. Rizzuto c, d, G. Antonelli a, b, D. Di Giuseppe a, b, M. D’Orazio a, b, J. Filippi a, b,
M.C. Comes a, b, P. Casti a, b, J.L. Vives Corrons c, M. Garcia-Bravo e, f, J.C. Segovia e, f, Maria del Mar Ma˜nú-Pereira g, M.J. Lopez-Martinez c, h, i, J. Samitier c, h, i, E. Martinelli a, b, *
a Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy
b Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, 00133 Rome, Italy c Institute for Bioengineering of Catalonia (IBEC) Barcelona Institute of Science and Technology BIST, Barcelona 08028, Spain
d Josep Carreras Leukaemia Research Institute (IJC), Badalona, 08916, Spain and with University of Barcelona, Department of Medicine, Barcelona 08036, Spain e Biomedical Innovation Unit, Centro de Investigaciones Energ´eticas, Medioambientales y Tecnol´ogicas (CIEMAT) and Centro de Investigaci´on Biom´edica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
f Unidad Mixta de Terapias Avanzadas. Instituto de Investigaci´on Sanitaria Fundaci´on Jim´enez (IIS-FJD, UAM), Madrid, Spain
g Vall d’Hebron Research Institute, Erithropatology Unit, Translational Research in Child and Adolescent Cancer – Rare anemia disorders research laboratory, Valld’Hebron Research Institute, ERN-EuroBloodNet member, Barcelona 08035, Spain
h Centro de Investigacion Biomedica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, 28029, Spain, University of Barcelona, Department of Electronic and Biomedical Engineering, Barcelona 08028, Spain
i University of Barcelona, Department of Electronic and Biomedical Engineering, Barcelona 08028, Spain | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Machine learning microfluidics Deep transfer learning
Video analysis
Blood disease | | Microfluidics represents a very promising technological solution for conducting massive biological experiments. However, the difficulty of managing the amount of information available often precludes the wide potential offered. Using machine learning, we aim to accelerate microfluidics uptake and lead to quantitative and reliable findings. In this work, we propose complementing microfluidics with machine learning (MLM) approaches to enhance the diagnostic capability of lab-on-chip devices. The introduction of data analysis methodologies within the deep learning framework corroborates the possibility of encoding cell morphology beyond the standard cell appearance. The proposed MLM platform is used in a diagnostic test for blood diseases in murine RBC samples ina dedicated microfluidics device in flow. The lack of plasticity of RBCs in Pyruvate Kinase Disease (PKD) is measured massively by recognizing the shape deformation in RBCs walking in a forest of pillars within the chip. Very high accuracy results, far over 85 %, in recognizing PKD from control RBCs either in simulated and in real experiments demonstrate the effectiveness of the platform. | 1. Introduction Today, one of the most challenging frontiers in system engineering is the possibility of recapitulating limited parts and activities of the human body in ex-vivo environments [1]. This is made possible by microfluidic devices. Microfluidic and Lab-on-a-chip (LOC) technologies [2–4] have attracted increasin

FAQs

How does the proposed approach combine microfluidics with machine learning?

It complements lab-on-chip microfluidics with machine learning methods, embedding data analysis inside the deep learning framework to improve diagnostic capability.

What is the diagnostic target and how is it evaluated in the platform?

The platform evaluates red blood cell plasticity defects associated with Pyruvate Kinase Disease by recognizing RBC shape deformation while cells move through a pillar array in the chip.

How accurate is the method for identifying PKD?

The study reports very high accuracy, exceeding 85%, for distinguishing PKD from control RBCs in both simulated and real experiments.

Aug 6, 2026

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