ArticleCommunications biology2024
A deep learning approach to predict differentiation outcomes in hypothalamic-pituitary organoids.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Engineering human multi-organ tissue chip niches for drug absorption, distribution, metabolism, excretion and toxicity prediction.Nature biomedical engineering · 2026Review
- Generation of Functional GnRH-Responsive Pituitary-like Organoids from Ovine Embryonic Stem Cells.Cells · 2026Article
- 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
- BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience.Bioengineering & translational medicine · 2026Article
- Endocrinology at a Miniature Level: Pluripotent Stem-Cell-Derived Organoid Models of Hypothalamus-Pituitary Axes.Biomolecules · 2026Review
- Autocrine and paracrine regulation of pituitary endocrine cells.Frontiers in endocrinology · 2026Review
- Agentic Lab: An Agentic-physical AI system for cell and organoid experimentation and manufacturing.bioRxiv : the preprint server for biology · 2025Article
- Bone organoid construction and evolution.Journal of orthopaedic translation · 2025Review
- Towards advanced regenerative therapeutics to tackle cardio-cerebrovascular diseases.American heart journal plus : cardiology research and practice · 2025Review
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
We use three-dimensional culture systems of human pluripotent stem cells for differentiation into pituitary organoids. Three-dimensional culture is inherently characterized by its ability to induce heterogeneous cell populations, making it difficult to maintain constant differentiation efficiency. That is why the culture process involves empirical aspects. In this study, we use deep-learning technology to create a model that can predict from images of organoids whether differentiation is progressing appropriately. Our models using EfficientNetV2-S or Vision Transformer, employing VENUS-coupled RAX expression, predictively class bright-field images of organoids into three categories with 70% accuracy, superior to expert-observer predictions. Furthermore, the model obtained by ensemble learning with the two algorithms can predict RAX expression in cells without RAX::VENUS, suggesting that our model can be deployed in clinical applications such as transplantation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.