Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques.

ABSTRACT: Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs. We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We clustered and refined the cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We al

SUBMITTER: Kowalczewski A 

PROVIDER: S-EPMC11228370 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature