AI Medical Compendium Journal:
Stem cells (Dayton, Ohio)

Showing 1 to 4 of 4 articles

Label-free quality control and identification of human keratinocyte stem cells by deep learning-based automated cell tracking.

Stem cells (Dayton, Ohio)
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 fo...

Potential applications of deep learning in single-cell RNA sequencing analysis for cell therapy and regenerative medicine.

Stem cells (Dayton, Ohio)
When used in cell therapy and regenerative medicine strategies, stem cells have potential to treat many previously incurable diseases. However, current application methods using stem cells are underdeveloped, as these cells are used directly regardle...

The global evolution and impact of systems biology and artificial intelligence in stem cell research and therapeutics development: a scoping review.

Stem cells (Dayton, Ohio)
Advanced bioinformatics analysis, such as systems biology (SysBio) and artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), is increasingly present in stem cell (SC) research. An approximate timeline on the...

Artificial Intelligence Supports Automated Characterization of Differentiated Human Pluripotent Stem Cells.

Stem cells (Dayton, Ohio)
Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy image...