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Neural stem cells have immense therapeutic potential for treating variousneurological disorders. However, lengthy differentiation protocols hinder thetranslation of neural stem cells into clinical applications. In this study, wepresent a deep learning approach using convolutional neural networks (CNNs) topredict the fate of neural stem cell differentiation at an early stage. Wetrained a CNN model on a dataset of cellular images from neural stem cellcultures, and the model achieved impressive results with a 99.7% testingaccuracy for the binary Resnet50 model and a 93.3% testing accuracy for themulticlass Resnet50 model in predicting neuron and glial cell differentiation.This demonstrates the feasibility of using CNNs for rapid, earlydifferentiation outcome prediction from simple microscopy images, which couldgreatly accelerate neural stem cell research and therapies. Additionally, themodel provides biological insights into morphological features associated withspecific neural cell lineages.

Predicting Neural Stem Cell Differentiation Using Deep Learning Models