Deep Learning-Enhanced Analysis for Delineating Anticoagulant Essay Efficacy Using Phase Microscopy
S. Shrivastava, M. Rathor, D. Yenurkar, S. K. Chaubey, S. Mukherjee, R. K. Singh

TL;DR
This study introduces a deep learning-based, label-free imaging framework using Digital Holographic Microscopy to evaluate and compare the efficacy of anticoagulants on blood samples by analyzing cellular morphology and coagulation dynamics.
Contribution
It presents a novel, non-invasive deep learning pipeline for high-throughput analysis of blood coagulation and anticoagulant effects using DHM, including a comparison of EDTA and KFeOx-NPs.
Findings
KFeOx-NPs prevent blood coagulation without altering RBC morphology
EDTA causes morphological changes within 6 hours
The system enables quantitative coagulation analysis over time
Abstract
The coagulation of blood after it is drawn from the body poses a significant challenge for hematological analysis, potentially leading to inaccurate test results and altered cellular characteristics, compromising diagnostic reliability. This paper presents a deep learning-enhanced framework for delineating anticoagulant efficacy ex vivo using Digital Holographic Microscopy (DHM). We demonstrate a label-free, non-invasive approach for analyzing human blood samples, capable of accurate cell counting and morphological estimation. A DHM with an automated image processing and deep learning pipeline is built for morphological analysis of the blood cells under two different anti-coagulation agents, e.g. conventional EDTA and novel potassium ferric oxalate nanoparticles (KFeOx-NPs). This enables automated high-throughput screening of cells and estimation of blood coagulation rates when samples…
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Taxonomy
TopicsDigital Holography and Microscopy · Advanced X-ray Imaging Techniques · Optical measurement and interference techniques
