Search Technologies

The National Institutes of Health - Clinical Center (NIH-CC) seeks to license and/or co-develop methods of reading chest x-rays using a deep learning models to detect a disease and describe its contents.

Computer automated segmentation of high variability organs and disease features in medical images is uniquely difficult. The application of deep learning and specialized neural networks may allow for automation of such interpretation tasks that are currently only performed by trained physicians. Computer automation may improve image analysis capabilities and lead to better diagnostics, disease monitoring, and surgical planning for many diseases. To help solve this challenge, researchers at the National Institutes of Health Clinical Center (NIHCC) have developed a technology that trains a computer to read and segment certain highly variable image features.

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.

The Office of the Director, National Cancer Institute is seeking statements of capability or interest from parties interested in collaborative research (using the Cooperative Research and Development Agreement (CRADA) or Material Transfer Agreement (MTA) to further develop, evaluate, or commercialize the software for accurate segmentation of cell nuclei and FISH signals in tissue sections. Collaborators working in the field of quantitative and automated pathology may be interested.

The National Institute of Health - Clinical Center (NIH-CC) seeks licensing and/or co-development of a system and method for tracking eye movement to increase the efficacy of visual diagnoses by radiologists.

Computer automated segmentation of high variability organs and disease features in medical images is uniquely difficult. The application of deep learning and specialized neural networks may allow for automation of such interpretation tasks that are currently only performed by trained physicians. Computer automation may improve image analysis capabilities and lead to better diagnostics, disease monitoring, and surgical planning for many diseases. To help solve this challenge, researchers at the National Institutes of Health Clinical Center (NIHCC) have developed a technology that trains a computer to read and segment certain highly variable image features, and this technology is available for licensing.

Researchers at the National Institute on Drug Abuse (NIDA) seek licensing and/or co-development research collaborations to further develop, evaluate or commercialize the software, Mobile Personalized Assessment & Learning for Addiction Treatment and Behavioral Modification. NIDA researchers developed this software for use in treating substance use disorders (drugs, alcohol, smoking) that provides personalized feedback to users.