A deep learning-based computational pipeline predicts developmental outcome in retinal organoids.

ABSTRACT: Retinal organoids have become important models for studying development and disease, yet stochastic heterogeneity in the formation of cell types, tissues, and phenotypes remains a major challenge. This limits our ability to precisely experimentally address the early developmental trajectories towards these outcomes. Here, we utilize deep learning to predict the differentiation path and resulting tissues in retinal organoids well before they become visually discernible. Our approach effectively bypasses the challenge of organoid-related heterogeneity in tissue formation. For this, we acquired a high-resolution time-lapse imaging dataset comprising about 1,000 organoids and over 100,000 images enabling precise temporal tracking of organoid development. By combining expert annotations with ad

SUBMITTER: Afting C 

PROVIDER: S-EPMC12843511 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature