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.

K. G. is a shareholder of two cell analysis startups (CYBO and Cupido). All other authors declare no competing interests.

This work was supported mainly by AMED JP20wm0325021 (M. N., K. G., Y. Y.) and JSPS Core-to-Core Program (K. G.) and partly by JSPS KAKENHI grant numbers 19H05633, 20H00317 (K. G.), and 21K15640 (M. N.), White Rock Foundation (K. G.), Ogasawara Foundation (K. G.), Nakatani Foundation (K. G.), Konica Minolta Foundation (K. G.), National Institutes of Health R01 GM130825 (G. K. R., MSER), National Science Foundation 1759802 (G. K. R. , M. S. E. R.), and Charitable Trust Laboratory Medicine Research Foundation of Japan (M. N.). C. Z. was supported by Epson International Scholarship.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

ethical approvals no. 11049 and no. 11344, granted by the Institutional Ethics Committee in the School of Medicine at the University of Tokyo

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

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All raw data and Python scripts for analysis are available on Zenodo: https://doi.org/10.5281/zenodo.6825004. The napari plugin is available on the napari-hub (https://www.napari-hub.org/plugins/disease-classifier).