AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Pluripotent Stem Cells

Showing 1 to 10 of 15 articles

Clear Filters

Viral pandemic preparedness: A pluripotent stem cell-based machine-learning platform for simulating SARS-CoV-2 infection to enable drug discovery and repurposing.

Stem cells translational medicine
Infection with the SARS-CoV-2 virus has rapidly become a global pandemic for which we were not prepared. Several clinical trials using previously approved drugs and drug combinations are urgently under way to improve the current situation. A vaccine ...

Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status.

Computers in biology and medicine
Human induced pluripotent stem cells (hiPSCs) are capable of differentiating into a variety of human tissue cells. They offer new opportunities for personalized medicine and drug screening. This requires large quantities of high quality hiPSCs, obtai...

Single-cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs.

Blood
Hematopoietic stem and progenitor cells (HSPCs) develop in distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) recapitulates some of these processes; however, i...

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...

Deep Learning Powered Identification of Differentiated Early Mesoderm Cells from Pluripotent Stem Cells.

Cells
Pluripotent stem cells can be differentiated into all three germ-layers including ecto-, endo-, and mesoderm in vitro. However, the early identification and rapid characterization of each germ-layer in response to chemical and physical induction of d...

Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing.

Nature methods
The rapid growth of single-cell transcriptomic technology has produced an increasing number of datasets for both embryonic development and in vitro pluripotent stem cell-derived models. This avalanche of data surrounding pluripotency and the process ...

A deep learning approach to predict differentiation outcomes in hypothalamic-pituitary organoids.

Communications biology
We use three-dimensional culture systems of human pluripotent stem cells for differentiation into pituitary organoids. Three-dimensional culture is inherently characterized by its ability to induce heterogeneous cell populations, making it difficult ...

A spatiotemporal and machine-learning platform facilitates the manufacturing of hPSC-derived esophageal mucosa.

Developmental cell
Human pluripotent stem cell-derived tissue engineering offers great promise for designer cell-based personalized therapeutics, but harnessing such potential requires a deeper understanding of tissue-level interactions. We previously developed a cell ...

Label-Free Detection of Biochemical Changes during Cortical Organoid Maturation via Raman Spectroscopy and Machine Learning.

Analytical chemistry
Human cerebral organoids have become valuable tools in neurodevelopment research, holding promise for investigating neurological diseases and reducing drug development costs. However, clinical translation and large-scale production of brain organoids...

What insights can spatiotemporal esophageal atlases and deep learning bring to engineering the esophageal mucosa?

Developmental cell
In this issue of Developmental Cell, Yang et al. present an integrated experimental and computational platform that maps the spatiotemporal development of the human esophagus and predicts key signaling pathways governing epithelial differentiation. T...