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Active or Not-Machine-learning based Prediction of Platelet Activation

Platelet activation and cell surface dynamics draws significant interest in the scientific community. The current work focuses on the question whether simple geometrical features derived from platelets scanned using standard light microscopy techniques are useful predictors of platelet shape change. The corresponding parameters, such as area or perimeter, were used as input for a supervised classification of a global platelet activation score and the distribution of the socalled pseudopodia. The latter are a striking sign of cell surface changes which is typically associated with the relevant activation of platelets. The results show that both phenotypes can be predicted with a high accuracy in unknown samples, demonstrating the potential of the automated platelet characterisation for a wide range of clinical and scientific applications.