ArticleScientific reports2017
A Machine Learning Assisted, Label-free, Non-invasive Approach for Somatic Reprogramming in Induced Pluripotent Stem Cell Colony Formation Detection and Prediction.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- From pixels to patterns: the AI revolution in stem cell-derived models.Nature methods · 2026Review
- Revolutionizing Stem Cell Sorting with Machine Learning: A Review of Trends, Tools, and Future Directions.Iranian journal of medical sciences · 2026Review
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- AI-Driven Quality Monitoring and Control in Stem Cell Cultures: A Comprehensive Review.Biotechnology journal · 2025Review
- Development of Standardized Methods and Analytical Tools.Advances in experimental medicine and biology · 2025Review
- How artificial intelligence can enable personalized mesenchymal stem cell-based therapeutic strategies in systemic lupus erythematosus.Frontiers in immunology · 2025Review
- Regenerative rehabilitation: a novel multidisciplinary field to maximize patient outcomes.Medical review (2021) · 2024Review
- Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Review
- AI-organoid integrated systems for biomedical studies and applications.Bioengineering & translational medicine · 2024Review
- Applications of artificial intelligence in regenerative dentistry: promoting stem cell therapy and the scaffold development.Frontiers in cell and developmental biology · 2024Review
- The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.PloS one · 2024Article
- Selecting Monoclonal Cell Lineages from Somatic Reprogramming Using Robotic-Based Spatial-Restricting Structured Flow.Research (Washington, D.C.) · 2024Article
- Comparison of miRNA transcriptome of exosomes in three categories of somatic cells with derived iPSCs.Scientific data · 2023Article
- A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems.Cell discovery · 2023Article
- SIC50: Determining drug inhibitory concentrations using a vision transformer and an optimized Sobel operator.Patterns (New York, N.Y.) · 2023Article
- Deep learning models for cancer stem cell detection: a brief review.Frontiers in immunology · 2023Review
- Understanding blood development and leukemia using sequencing-based technologies and human cell systems.Frontiers in molecular biosciences · 2023Review
- Morphology-based deep learning approach for predicting adipogenic and osteogenic differentiation of human mesenchymal stem cells (hMSCs).Frontiers in cell and developmental biology · 2023Article
- Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks.International journal of molecular sciences · 2022Article
- Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review.Biology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During cellular reprogramming, the mesenchymal-to-epithelial transition is accompanied by changes in morphology, which occur prior to iPSC colony formation. The current approach for detecting morphological changes associated with reprogramming purely relies on human experiences, which involve intensive amounts of upfront training, human error with limited quality control and batch-to-batch variations. Here, we report a time-lapse-based bright-field imaging analysis system that allows us to implement a label-free, non-invasive approach to measure morphological dynamics. To automatically analyse and determine iPSC colony formation, a machine learning-based classification, segmentation, and statistical modelling system was developed to guide colony selection. The system can detect and monitor the earliest cellular texture changes after the induction of reprogramming in human somatic cells on day 7 from the 20-24 day process. Moreover, after determining the reprogramming process and iPSC colony formation quantitatively, a mathematical model was developed to statistically predict the best iPSC selection phase independent of any other resources. All the computational detection and prediction experiments were evaluated using a validation dataset, and biological verification was performed. These algorithm-detected colonies show no significant differences (Pearson Coefficient) in terms of their biological features compared to the manually processed colonies using standard molecular approaches.
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Registered trials
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