Samuel Molčan

ORCID: 0000-0003-2059-7484
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About
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Research Areas
  • Digital Imaging for Blood Diseases
  • Erythrocyte Function and Pathophysiology
  • Blood properties and coagulation

University of Žilina
2022-2024

The elasticity of red blood cells (RBCs) plays a vital role in their efficient movement through vessels, facilitating the transportation oxygen within bloodstream. However, various diseases significantly impact RBC elasticity, making it an important parameter for diagnosing and monitoring health conditions. In this study, we propose novel approach to determine by analyzing video recordings using convolutional neural network (CNN) classification. Due scarcity available flow recordings,...

10.3390/app13137967 article EN cc-by Applied Sciences 2023-07-07

RBC (Red Blood Cell) membrane is a highly elastic structure, and proper modelling of this elasticity essential for biomedical applications that involve computational experiments with blood flow. In work, we present new method estimating one the key parameters red cell elasticity, which uses neural network trained on simulation outputs. We test classic LSTM (Long-Short Term Memory) architecture time series regression task, also experiment novel CNN-LSTM (Convolutional Neural Network)...

10.3390/sym14081732 article EN Symmetry 2022-08-19

The elasticity of red blood cells (RBCs) is crucial for their ability to fulfill role in the blood. Decreased RBC deformability associated with various pathological conditions. This study explores application machine learning predict RBCs using both image data and detailed physical measurements derived from simulations. We simulated behavior a microfluidic channel. simulation results provided basis generating on which we applied techniques. analyzed surface-area-to-volume ratio as an...

10.20944/preprints202408.1610.v1 preprint EN 2024-08-22

The elasticity of red blood cells (RBCs) is crucial for their ability to fulfill role in the blood. Decreased RBC deformability associated with various pathological conditions. This study explores application machine learning predict RBCs using both image data and detailed physical measurements derived from simulations. We simulated behavior a microfluidic channel. simulation results provided basis generating on which we applied techniques. analyzed surface-area-to-volume ratio as an...

10.3390/app14209315 article EN cc-by Applied Sciences 2024-10-12
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