ArticleStem cells (Dayton, Ohio)2021
Label-free quality control and identification of human keratinocyte stem cells by deep learning-based automated cell tracking.
Article in Stem cells (Dayton, Ohio), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 30 citations in OpenAlex.
- Coarse-graining analysis for non-invasive, label-free, and quantitative evaluation of undulating motion in cultured oral mucosa epithelial cell sheets.Regenerative therapy · 2026Article
- From pixels to patterns: the AI revolution in stem cell-derived models.Nature methods · 2026Review
- Accelerated discovery of cell migration regulators using label-free deep learning-based automated tracking.Science advances · 2026Article
- Review
- Current status, challenges, and prospects of artificial intelligence applications in wound repair theranostics.Theranostics · 2025Review
- Regenerative rehabilitation: a novel multidisciplinary field to maximize patient outcomes.Medical review (2021) · 2024Review
- Epidermal stem cells: skin surveillance and clinical perspective.Journal of translational medicine · 2024Review
- Artificial Intelligence Supports Automated Characterization of Differentiated Human Pluripotent Stem Cells.Stem cells (Dayton, Ohio) · 2023Article
- Differentiable optimization layers enhance GNN-based mitosis detection.Scientific reports · 2023Article
- Blank Spots in the Map of Human Skin: The Challenge for Xenotransplantation.International journal of molecular sciences · 2023Review
- Engineered biomaterials in stem cell-based regenerative medicine.Life medicine · 2023Review
- The EGF/EGFR axis and its downstream signaling pathways regulate the motility and proliferation of cultured oral keratinocytes.FEBS open bio · 2023Article
- Deep Learning of Phase-Contrast Images of Cancer Stem Cells Using a Selected Dataset of High Accuracy Value Using Conditional Generative Adversarial Networks.International journal of molecular sciences · 2023Article
- EGFR-mediated epidermal stem cell motility drives skin regeneration through COL17A1 proteolysis.The Journal of cell biology · 2021Article
- Label-free quality control and identification of human keratinocyte stem cells by deep learning-based automated cell tracking.Stem cells (Dayton, Ohio) · 2021Article
- Cells/colony motion of oral keratinocytes determined by non-invasive and quantitative measurement using optical flow predicts epithelial regenerative capacity.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stem cell-based products have clinical and industrial applications. Thus, there is a need to develop quality control methods to standardize stem cell manufacturing. Here, we report a deep learning-based automated cell tracking (DeepACT) technology for noninvasive quality control and identification of cultured human stem cells. The combination of deep learning-based cascading cell detection and Kalman filter algorithm-based tracking successfully tracked the individual cells within the densely packed human epidermal keratinocyte colonies in the phase-contrast images of the culture. DeepACT rapidly analyzed the motion of individual keratinocytes, which enabled the quantitative evaluation of keratinocyte dynamics in response to changes in culture conditions. Furthermore, DeepACT can distinguish keratinocyte stem cell colonies from non-stem cell-derived colonies by analyzing the spatial and velocity information of cells. This system can be widely applied to stem cell cultures used in regenerative medicine and provides a platform for developing reliable and noninvasive quality control technology.
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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.