{"doi":"10.1101/2022.09.13.22279890","title":"Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates","abstract":"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.","journal":"medRxiv","year":2022,"id":304371,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9482,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":908228,"name":"Maik Herbig","orcid":"0000-0001-7592-7829","position":1,"is_corresponding":false},{"id":642709,"name":"Yuqi Zhou","orcid":"0000-0002-1206-0049","position":2,"is_corresponding":false},{"id":642707,"name":"Masako Nishikawa","orcid":"0000-0002-3840-767X","position":3,"is_corresponding":false},{"id":317800,"name":"Mohammad Shifat‐E‐Rabbi","orcid":"0000-0002-0972-5353","position":4,"is_corresponding":false},{"id":642708,"name":"Hiroshi Kanno","orcid":"0000-0003-3340-9462","position":5,"is_corresponding":false},{"id":839774,"name":"Ruoxi Yang","orcid":"0000-0001-8225-5856","position":6,"is_corresponding":false},{"id":643737,"name":"Yuma Ibayashi","orcid":null,"position":7,"is_corresponding":false},{"id":642710,"name":"Ting‐Hui Xiao","orcid":"0000-0002-7339-152X","position":8,"is_corresponding":false},{"id":317802,"name":"Gustavo K. Rohde","orcid":"0000-0003-1703-9035","position":9,"is_corresponding":false},{"id":997747,"name":"Masataka Sato","orcid":null,"position":10,"is_corresponding":false},{"id":997210,"name":"Satoshi Kodera","orcid":"0000-0003-2908-6875","position":11,"is_corresponding":false},{"id":671078,"name":"Masao Daimon","orcid":"0000-0002-7616-3477","position":12,"is_corresponding":false},{"id":642720,"name":"Yutaka Yatomi","orcid":"0000-0003-1719-4297","position":13,"is_corresponding":false},{"id":642721,"name":"Keisuke Goda","orcid":"0000-0001-6302-6038","position":14,"is_corresponding":false},{"id":997209,"name":"Chenqi Zhang","orcid":"0000-0003-3241-821X","position":0,"is_corresponding":true}],"reference_count":16,"raw_metadata":null,"created_at":"2026-07-19T00:32:32.651796Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}