Using Deep Learning to Predict Neural Stem Cell Differentiation in Regenerative Medicine (2312.06665v2)

Abstract: Over one in three people are affected by neurodegenerative disorders. Neural stem cells, which are multipotent regenerative cells with the potential to differentiate into any of the neural cell types, have immense therapeutic potential for treating neurological disorders. However, lengthy differentiation protocols hinder clinical applications and research. 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. Our models achieved impressive results in predicting neuron and glial cell differentiation, with a 93.3% testing accuracy for a multiclass Resnet50 model (and 99.7% accuracy for a binary Resnet50 model). In addition, we developed and published a web tool to give stem cell researchers access to this technology to allow for efficient prediction of stem cell cell differentiation. Our work demonstrates the feasibility of and builds tooling for using CNNs for rapid, early differentiation outcome prediction from simple microscopy images, which could greatly accelerate neural stem cell research and therapies.