AI-based morphology analysis for evaluating neural differentiation and its application technologies

Abstract

Neural Stem Cells (NSCs) hold transformative potential in embryonic neurogenesis research, disease pathogenesis modeling, and drug-screening system design due to their capacity to differentiate into specific neuronal cells. Despite their promise, technical challenges pertaining to purity, reproducibility, and toxicity hinder the acquisition of high-quality Neural Progenitor Cells (NPCs). To address these challenges, this study utilizes morphology-based artificial intelligence (AI) analysis on neural rosette images to identify optimally conditioned cells. Our AI-driven morphological examination revealed distinct morphotypes within neural rosettes, a factor we found directly correlated with the reliability of neural induction during the neural differentiation process. These findings underline the considerable potential of morphology-based AI analysis as an innovative approach to enhance neural stem cell culture protocols, thereby providing meaningful contributions to the fields of neuroscience and cell biology.