Evaluating chemical effects on human neural cells through calcium imaging and deep learning

New substances intended for human consumption must undergo extensive preclinical safety pharmacology testing prior to approval. These tests encompass the evaluation of effects on the central nervous system, which is highly sensitive to chemical substances. With the growing understanding of the species-specific characteristics of human neural cells and advancements in machine learning technology, the development of effective and efficient methods for the initial screening of chemical effects on human neural function using machine learning platforms is anticipated. In this study, we employed a deep learning model to analyze calcium dynamics in human-induced pluripotent stem cell-derived neural progenitor cells, which were exposed to various concentrations of four representative chemicals. We report that this approach offers a reliable and concise method for quantitatively classifying the effects of chemical exposures and predicting potential harm to human neural cells.