ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2023
Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Single-pixel imaging flow cytometry for biomedical research.Inflammation and regeneration · 2025Review
- Linear Optimal Transport Subspaces for Point Set Classification.Journal of mathematical imaging and vision · 2025Article
- Direct evaluation of antiplatelet therapy in coronary artery disease by comprehensive image-based profiling of circulating platelets.Nature communications · 2025Article
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- Imaging flow cytometry as a novel approach for the diagnosis of heparin-induced thrombocytopenia.British journal of haematology · 2025Article
- Quantifying Nuclear Structures of Digital Pathology Images Across Cancers Using Transport-Based Morphometry.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2025Article
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Authors and funding
15 authors.
Funding
Abstract
Microvascular thrombosis is a typical symptom of COVID-19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID-19 samples and 101 non-COVID-19 thrombosis samples, resulting in a total of 6.3 million bright-field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on derived single-cell features for each population, we trained machine learning models for classification between COVID-19 and non-COVID-19 thrombosis, resulting in a patient testing accuracy of 75%. This result indicates that platelet formation differs between COVID-19 and non-COVID-19 thrombosis. All analysis steps were optimized for efficiency and implemented in an easy-to-use plugin for the image viewer napari, allowing the entire analysis to be performed within seconds on mid-range computers, which could be used for real-time diagnosis.
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Registered trials
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