ArticleComputational and mathematical methods in medicine2016
Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images.
Article in Computational and mathematical methods in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- 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
- Clinical translation of human iPSC technologies: advances, safety concerns, and future directions.Frontiers in cell and developmental biology · 2025Review
- Non-invasive maturity assessment of iPSC-CMs based on optical maturity characteristics using interpretable AI.Computational and structural biotechnology journal · 2025Article
- 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
- The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.PloS one · 2024Article
- Morphological Signal Processing for Phenotype Recognition of Human Pluripotent Stem Cells Using Machine Learning Methods.Biomedicines · 2023Article
- Engineering considerations of iPSC-based personalized medicine.Biomaterials research · 2023Review
- SIC50: Determining drug inhibitory concentrations using a vision transformer and an optimized Sobel operator.Patterns (New York, N.Y.) · 2023Article
- Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review.Biology · 2022Article
- Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Bioengineering Strategies to Create 3D Cardiac Constructs from Human Induced Pluripotent Stem Cells.Bioengineering (Basel, Switzerland) · 2022Review
- Moving Towards Induced Pluripotent Stem Cell-based Therapies with Artificial Intelligence and Machine Learning.Stem cell reviews and reports · 2022Review
- Recent trends in stem cell-based therapies and applications of artificial intelligence in regenerative medicine.World journal of stem cells · 2021Review
- Analysis of Drug Effects on iPSC Cardiomyocytes with Machine Learning.Annals of biomedical engineering · 2021Article
- Tracking and Predicting Human Somatic Cell Reprogramming Using Nuclear Characteristics.Biophysical journal · 2020Article
- A Machine Learning Assisted, Label-free, Non-invasive Approach for Somatic Reprogramming in Induced Pluripotent Stem Cell Colony Formation Detection and Prediction.Scientific reports · 2017Article
- Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software.Stem cell reports · 2017Article
- Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells.PloS one · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using a k-NN classifier showing improved accuracy compared to earlier studies.
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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.