Convolutional neuronal network for identifying single-cell-platelet-platelet-aggregates in human whole blood using imaging flow cytometry.

Imaging flow cytometry is an attractive method to investigate individual cells by optical properties. However, imaging flow cytometry applications with clinical relevance are scarce so far. Platelet aggregation naturally occurs during blood coagulation to form a clot. However, aberrant platelet aggregation is associated with cardiovascular disease under steady-state conditions in the blood. Several types of so-called antiplatelet drugs are frequently described to reduce the risk of stroke or cardiovascular diseases. However, an efficient monitoring method is missing to identify the presence and frequency of platelet-platelet aggregates in whole blood on a single cell level. In this work, we employed imaging flow cytometry to identify fluorescently labeled platelets in whole blood with a conditional gating strategy. Images were post-processed and aligned. A convolutional neural network was designed to identify platelet-platelet aggregates of two, three, and more than three platelets, and results were validated against various data set properties. In addition, the neural network excluded erythrocyte-platelet aggregates from the results. Based on the results, a parameter for detecting platelet-platelet aggregates, the weighted platelet aggregation, was developed. If employed on a broad scale with proband and patient samples, our method could aid in building a future diagnostic marker for cardiovascular disease and monitoring parameters to optimize drug prescriptions in such patient groups.