Deep learning-based predictive identification of neural stem cell differentiation.

ABSTRACT: The differentiation of neural stem cells (NSCs) into neurons is proposed to be critical in devising potential cell-based therapeutic strategies for central nervous system (CNS) diseases, however, the determination and prediction of differentiation is complex and not yet clearly established, especially at the early stage. We hypothesize that deep learning could extract minutiae from large-scale datasets, and present a deep neural network model for predictable reliable identification of NSCs fate. Remarkably, using only bright field images without artificial labelling, our model is surprisingly effective at identifying the differentiated cell types, even as early as 1 day of culture. Moreover, our approach showcases superior precision and robustness in designed independent test scenarios inv

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC8110743 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature