  Neural stem cells have immense therapeutic potential for treating various
neurological disorders. However, lengthy differentiation protocols hinder the
translation of neural stem cells into clinical applications. In this study, we
present a deep learning approach using convolutional neural networks (CNNs) to
predict the fate of neural stem cell differentiation at an early stage. We
trained a CNN model on a dataset of cellular images from neural stem cell
cultures, and the model achieved impressive results with a 99.7% testing
accuracy for the binary Resnet50 model and a 93.3% testing accuracy for the
multiclass Resnet50 model in predicting neuron and glial cell differentiation.
This demonstrates the feasibility of using CNNs for rapid, early
differentiation outcome prediction from simple microscopy images, which could
greatly accelerate neural stem cell research and therapies. Additionally, the
model provides biological insights into morphological features associated with
specific neural cell lineages.
