During (1) the training process, automatic feature extraction from the annotated dataset occurred, while in (2) the prediction step, the obtained residual neural network model classified unlabelled single-particle images. The research is supported by the Russian Science Foundation (grant 21-73-00097). "If one wants to evaluate the effects of microplastics on humans and animals, then among the primary instruments will be a technology to detect microplastics in the organisms or cells. We tried to find out which plastic particles penetrate better into living cells, which ones are more detrimental, where they are localized, and how to distinguish one type of plastic in the body from another," says Dr. Gölnur Fakhrullina, Research Associate of KFU's Bionanotechnology Lab and the principal investigator of this project. The technique used in the publication is based on the imaging the live cells using dark-field microscopy.