Artificial Intelligence Supports Automated Characterization of Differentiated Human Pluripotent Stem Cells

Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to, that is, distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation toward hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell cultures.

Automated summary

Revolutionary advances in AI and deep learning have led to an increase in research papers exploring the applications of these technologies in the biomedical field. This study investigates the use of a deep learning model to predict the differentiation stage of pluripotent stem cells towards hepatocytes based on morphological features. The results show that the model can accurately classify images from early and late differentiation time points, aligning well with experimental validation of cell identity and function. This suggests that deep learning models can provide a semi-automated method for characterizing stem cell cultures.