ArticleStem cell reports2018
Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells.
Article in Stem cell reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 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
45 citing papers in PubMed, 108 citations in OpenAlex.
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- Revolutionizing Stem Cell Sorting with Machine Learning: A Review of Trends, Tools, and Future Directions.Iranian journal of medical sciences · 2026Review
- BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience.Bioengineering & translational medicine · 2026Article
- Artificial intelligence-driven nano-enhanced stem cell therapy for neurodegenerative diseases: from rational design to clinical translation.Journal of nanobiotechnology · 2026Review
- Chondrogenic Differentiation of Stem Cells for Cartilage Regeneration: Advances and Future Perspectives.Tissue engineering and regenerative medicine · 2026Review
- Early and non-destructive prediction of the differentiation efficiency of human induced pluripotent stem cells using imaging and machine learning.Scientific reports · 2025Article
- AI-based virtual immunocytochemistry for rapid and robust fine needle aspiration biopsy diagnosis.Diagnostic pathology · 2025Article
- Induced Pluripotent Stem Cells in Cardiomyopathy: Advancing Disease Modeling, Therapeutic Development, and Regenerative Therapy.International journal of molecular sciences · 2025Review
- Clinical trials in-a-dish for cardiovascular medicine.European heart journal · 2024Review
- A literature review: machine learning-based stem cell investigation.Annals of translational medicine · 2024Review
- AI-organoid integrated systems for biomedical studies and applications.Bioengineering & translational medicine · 2024Review
- The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.PloS one · 2024Article
- Machine learning-based estimation of spatial gene expression pattern during ESC-derived retinal organoid development.Scientific reports · 2023Article
- Deep learning-based identification of sinoatrial node-like pacemaker cells from SHOX2/HCN4 double-positive cells differentiated from human iPS cells.Journal of arrhythmia · 2023Article
- A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems.Cell discovery · 2023Article
- Label-free optical imaging and sensing for quality control of stem cell manufacturing.Current opinion in biomedical engineering · 2023Article
- SIC50: Determining drug inhibitory concentrations using a vision transformer and an optimized Sobel operator.Patterns (New York, N.Y.) · 2023Article
- Deep learning models will shape the future of stem cell research.Stem cell reports · 2023Review
- Article
- Morphology-based deep learning approach for predicting adipogenic and osteogenic differentiation of human mesenchymal stem cells (hMSCs).Frontiers in cell and developmental biology · 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
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning technology is rapidly advancing and is now used to solve complex problems. Here, we used deep learning in convolutional neural networks to establish an automated method to identify endothelial cells derived from induced pluripotent stem cells (iPSCs), without the need for immunostaining or lineage tracing. Networks were trained to predict whether phase-contrast images contain endothelial cells based on morphology only. Predictions were validated by comparison to immunofluorescence staining for CD31, a marker of endothelial cells. Method parameters were then automatically and iteratively optimized to increase prediction accuracy. We found that prediction accuracy was correlated with network depth and pixel size of images to be analyzed. Finally, K-fold cross-validation confirmed that optimized convolutional neural networks can identify endothelial cells with high performance, based only on morphology.
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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.