Real‐time intelligent classification of COVID‐19 and thrombosis via massive image‐based analysis of platelet aggregates

Abstract

AbstractMicrovascular 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.