ArticleStem cell reports2019
Deep Learning Neural Networks Highly Predict Very Early Onset of Pluripotent Stem Cell Differentiation.
Article in Stem cell reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 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
52 citing papers in PubMed.
- An artificial intelligence optimized hepatic differentiation unveils NR5A2 and AP-1 transcriptional regulation in hepatic maturation.The Journal of biological chemistry · 2026Article
- Revolutionizing Stem Cell Sorting with Machine Learning: A Review of Trends, Tools, and Future Directions.Iranian journal of medical sciences · 2026Review
- Deep learning for predicting stem cell efficiency for use in beta cell differentiation.Scientific reports · 2026Article
- Development and validation of deep learning for predicting the growth of ovarian cancer organoids.Chinese medical journal · 2026Article
- Chondrogenic Differentiation of Stem Cells for Cartilage Regeneration: Advances and Future Perspectives.Tissue engineering and regenerative medicine · 2026Review
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Advancing cell therapies with artificial intelligence and synthetic biology.Current opinion in biomedical engineering · 2025Article
- Deep learning-based image classification reveals heterogeneous execution of cell death fates during viral infection.Molecular biology of the cell · 2025Article
- How artificial intelligence can enable personalized mesenchymal stem cell-based therapeutic strategies in systemic lupus erythematosus.Frontiers in immunology · 2025Review
- Revolutionizing Epithelial Differentiability Analysis in Small Airway-on-a-Chip Models Using Label-Free Imaging and Computational Techniques.Biosensors · 2024Article
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- Neural Network-Based Filter Design for Compressive Raman Classification of Cells.Journal of chemical information and modeling · 2024Article
- Noninvasive, label-free image approaches to predict multimodal molecular markers in pluripotency assessment.Scientific reports · 2024Article
- A literature review: machine learning-based stem cell investigation.Annals of translational medicine · 2024Review
- Semi-automated, high-content imaging of drug transporter knockout sea urchin (Lytechinus pictus) embryos.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2024Article
- Cancer Stem Cells from Definition to Detection and Targeted Drugs.International journal of molecular sciences · 2024Review
- Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.Stem cell research & therapy · 2024Article
- Iterative pseudo balancing for stem cell microscopy image classification.Scientific reports · 2024Article
- Applications of artificial intelligence in regenerative dentistry: promoting stem cell therapy and the scaffold development.Frontiers in cell and developmental biology · 2024Review
- Machine learning-based estimation of spatial gene expression pattern during ESC-derived retinal organoid development.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning is a significant step forward for developing autonomous tasks. One of its branches, computer vision, allows image recognition with high accuracy thanks to the use of convolutional neural networks (CNNs). Our goal was to train a CNN with transmitted light microscopy images to distinguish pluripotent stem cells from early differentiating cells. We induced differentiation of mouse embryonic stem cells to epiblast-like cells and took images at several time points from the initial stimulus. We found that the networks can be trained to recognize undifferentiated cells from differentiating cells with an accuracy higher than 99%. Successful prediction started just 20 min after the onset of differentiation. Furthermore, CNNs displayed great performance in several similar pluripotent stem cell (PSC) settings, including mesoderm differentiation in human induced PSCs. Accurate cellular morphology recognition in a simple microscopic set up may have a significant impact on how cell assays are performed in the near future.
Indexed as
Identifiers
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.