02025nas a2200241   4500000000100000000000100001008004100002260001500043653001700058653004500075653001500120100002400135700002100159700002300180700001800203700001700221700002900238700002400267245009100291856005500382520133200437022001401769       2026                            d  c2026-03-0910aCell biology10aComputational biology and bioinformatics10aStem cells1 aFranziska J. Schöb1 aAlexander Binder1 aValentina Zamarian1 aValeria Sordi1 aHanne Scholz1 aAnders Malthe-Sørenssen1 aDag Kristian Dysthe00aDeep learning for predicting stem cell efficiency for use in beta cell differentiation  uhttps://www.nature.com/articles/s41598-026-42830-33 aRecent clinical trial data show curative potential of cell therapy for diabetes, however the cells required are a bottleneck. Cell differentiation exhibits substantial variability, even among clones of stem cells generated from the same patient. Human experts struggle to see the difference between highly- and lowly-efficient cell clones early. We therefore propose an image-based deep learning model to guide the selection of the most efficient clones. We apply different deep learning models to learn the morphological differences between good and bad stem cell clones and classify them based on phase-contrast imaging. To gain insight into the learned features, we use layer-wise relevance propagation, and Fourier-based frequency analysis. Using an EfficientNet-V2-S model, we obtain a novel early prediction for the outcome of the differentiation process from patient-derived stem cells to $$\upbeta$$ -cells using imaging. Clone level accuracy is 96.7 % at 53 hours after start of differentiation. The analysis of learned features shows that the structure of the cell population is an important predictive feature. This study is a proof-of-concept that deep learning combined with label-free imaging can be highly predictive and guide selection of stem cell clones, thereby reducing cost of $$\upbeta$$ -cell production.  a2045-2322