Search Technologies

Researchers at the National Institutes for Health Clinical Center (NIHCC) have developed computer-aided diagnostics (CAD) that may further improve the already superior capabilities of multiparametric magnetic resonance imaging (MRI) for detection and imaging of prostate cancer. This system produces an accurate probability map of potential cancerous lesions in multiparametric MRI images that is superior to other systems and may have multiple product applications.

The Frederick National Laboratory for Cancer Research seeks parties interested in collaborative research to co-develop software for the automatic 3-D visualization of biological image volumes.

Available for licensing is computer software for the automated generation of density maps of macromolecular structures from a series of 2D digital micrographs of frozen hydrated specimens collected using an electron microscope equipped with an ultra-cooled computerized stage.

The National Cancer Institute (NCI) seeks research, co-development, or licensing partners for software that uses computational approaches in cancer diagnosis. NCI researchers have recently developed a computational approach for detecting, quantifying, and mapping Mitotic Hotspots in whole slide images of tumor tissue. This technology has demonstrated high reproducibility that is unaffected by diagnostic skill or fatigue, allowing standardization of tumor cell proliferation assessment across institutions.

Researchers at the National Institute of Health (NIH) and National Institute of Standards and Technology (NIST) seek licensing or co-development partners for a method to predict functions, identity, disease state, and health of stem cells using machine learning.

The National Cancer Institute (NCI) seeks parties interested in licensing software for the targeted delivery of anti-cancer agents in solid tumors.

Hospital Picture Archiving and Communication Systems (PACS) contain vast amounts of underutilized informatics about disease conditions. As computer image processing and systems advance, PACS informatics may form the foundation for precision automated computer-aided diagnostics for a wide range of disease conditions. Development of such systems may improve diagnostic accuracy and better inform treatment, but creating systems and algorithms capable of “learning” to recognize and locate the image patterns of disease and associated labels is a difficult problem. Researchers at the National Institutes of Health Clinical Center (NIHCC) have developed a technology that applies deep learning to PACS images to produce a database where certain disease features are identified and spatially located. Researchers at the NIHCC seek licensing of the PACS.

The Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD) seeks research and co-development partners or licensees for an invention that discloses the diagnosis of pathologies in tissue related to changes in cell size, cellularity, cell infiltration, and other abnormalities detected by bulk water diffusion changes.

Extracellular vesicles (EVs) are lipid spheres released from cells. EVs contain proteins that can serve as diagnostic biomarkers indicating the cell state at time of release. Improved detection and phenotyping of EVs and their protein cargo could lead to better cancer diagnostic and prognostic tests, as well as improved therapeutic uses. The National Cancer Institute (NCI) seeks research co-development partners and/or licensees for a software package that performs high-throughput multi-dimensional analysis of EV biomarkers.