ArticleCell reports methods2025
Prediction of the hypothalamus-pituitary organoid formation using machine learning.
Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Artificial intelligence virtual bone organoids (AIVBOs).Journal of orthopaedic translation · 2026Review
- Emerging human cell-based in vitro models for studying pituitary development and disorders.Endocrine journal · 2026Review
- Next-Generation Strategies for Neural Repair and Regeneration: Neural Organoid Transplantation in the CNS.Cell proliferation · 2026Review
- Endocrinology at a Miniature Level: Pluripotent Stem-Cell-Derived Organoid Models of Hypothalamus-Pituitary Axes.Biomolecules · 2026Review
- Advances in organoids for personalized medicine: from technological development to clinical application.Frontiers in cell and developmental biology · 2026Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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 differentiation at day 40. Moreover, the computational approach identified the shape of the organoid surface as a critical determining factor that significantly affected the prediction. This model can help to enhance the efficiency of organoid induction experiments and illuminate the molecular mechanisms involved in hypothalamus-pituitary differentiation.
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Registered trials
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