Machine Learning and/or Neural Networks to Validate Stem Cells and Their Derivatives for Use in Cell Therapy, Drug Delivery, and Diagnostics

Many biological and clinical procedures require functional validation of a desired cell type. Current techniques to validate rely on various assays and methods, such as staining with dyes, antibodies, and nucleic acid probes, to assess stem cell health, death, proliferation, and functionality. These techniques potentially destroy stem cells and risk contaminating cells and cultures by exposing them to the environment; they are low-throughput and difficult to scale-up. Therefore, there is a significant need for potentially less invasive, scalable, higher throughput methods of validation, while maintaining quality and accuracy.Trained biologists can often recognize cells and phenotypic cell function based on cell appearance under a microscope. Applying machine learning to perform similar visual analysis may have many advantages. Computer-automated, image-based validation of a cell ’s function does not require exposing the cells to the environment, thus significantly reducing the chance of cell contamination or destruction. Furthermore, such technology could be more inexpensive to implement than other validation or quality controls. It even has potential for high throughput use, to be scaled up, for many applications in research, manufacturing, and cell therapeutics settings. Scientists at the National Cancer Institute (NCI) and National Institute of Standards and Technology (NIST) have developed an image-based machine learning system that is able to validate functional cell ...
Source: NIH OTT Licensing Opportunities - Category: Research Authors: Source Type: research