ArticleNature communications2025
AI-based approach to dissect the variability of mouse stem cell-derived embryo models.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
8 citing papers in PubMed.
- From pixels to patterns: the AI revolution in stem cell-derived models.Nature methods · 2026Review
- Epigenetic blind spots - the role of DNA methylation dynamics in stem cell-based models of embryogenesis.FEBS letters · 2026Article
- Decoding the origins of cellular self-organization for engineered biology.Nature biotechnology · 2026Review
- Synthetic embryos: current progress, application and challenges.Reproductive biology and endocrinology : RB&E · 2026Review
- Primate embryoid models: functional interrogation of late gastrulation.Signal transduction and targeted therapy · 2026Article
- Progress in stem cell-based embryo models and their applications in developmental biology and biomedicine.Nature reviews. Molecular cell biology · 2026Review
- deepBlastoid: a deep learning model for automated and efficient evaluation of human blastoids.Life medicine · 2025Article
- Advances in engineered models of peri-gastrulation.iScience · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Recent advances in stem cell-derived embryo models have transformed developmental biology, offering insights into embryogenesis without the constraints of natural embryos. However, variability in their development challenges research standardization. To address this, we use deep learning to enhance the reproducibility of selecting stem cell-derived embryo models. Through live imaging and AI-based models, we classify 900 mouse post-implantation stem cell-derived embryo-like structures (ETiX-embryos) into normal and abnormal categories. Our best-performing model achieves 88% accuracy at 90 h post-cell seeding and 65% accuracy at the initial cell-seeding stage, forecasting developmental trajectories. Our analysis reveals that normally developed ETiX-embryos have higher cell counts and distinct morphological features such as larger size and more compact shape. Perturbation experiments increasing initial cell numbers further supported this finding by improving normal development outcomes. This study demonstrates deep learning's utility in improving embryo model selection and reveals critical features of ETiX-embryo self-organization, advancing consistency in this evolving field.
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What OpenQuestion holds
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.