Prediction of the hypothalamus-pituitary organoid formation using machine learning.

ABSTRACT: Multi-cellular organoids are self-assembly aggregates that mimic biological functions and developmental processes of many tissue types in vitro. They are widely employed for disease modeling and functional studies. Hypothalamus-pituitary organoids can be generated through differentiation induction from pluripotent stem cells. However, their maturation is time consuming and labor intensive, and the quality of the resulting organoids can vary. Here, we developed a machine learning model capable of accurately predicting the successful generation of high-quality hypothalamus-pituitary organoids based solely on phase-contrast images captured during the early stage of differentiation. The model achieved an accuracy of 79% using images from organoids on day 9 to predict pituitary cell differentia

SUBMITTER: Matsumoto R 

PROVIDER: S-EPMC12461651 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature