PREDICTIVE NEURAL STEM CELL DIFFERENTIATION USING SINGLE-CELL IMAGES BASED ON DEEP LEARNING

Abstract

The process of neural stem cell (NSC) differentiation into neurons is crucial for the development of potential cell-centered treatments for central nervous system disorders. However, predicting, identifying, and anticipating this differentiation is complex. In this study, we propose the implementation of a convolutional neural network model for the predictable recognition of NSC fate, utilizing single-cell brightfield images. The results demonstrate the model’s effectiveness in predicting NSC differentiation into astrocytes, neurons, and oligodendrocytes, achieving an accuracy rate of 91.27%, 93.69%, and 93.06%, respectively. Moreover, our proposed model effectively distinguishes between various cell types even within the initial day of culture.