Search Technologies

The National Cancer Institute (NCI) seeks licensees for a novel computer program that converts existing CT scan output into organ radiation doses.

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.

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.

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.

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.

The National Institute on Drug Abuse (NIDA) seeks licensing and/or co-development research collaborations for use of software for substance use disorders, behavior modification, and cancer patient care and pain management, etc. NIDA has developed software that permits real-time communication of patient-reported data and associated geolocation data. The software can be used in patient treatment or as a research tool for evaluating effectiveness of treatments.

Researchers at the National Institutes of Health Clinical Center (NIHCC) developed a technology that improves segmentation detail levels for anatomical structures in medical images through a new, deep learning approach. Difficult anatomical features, often segmented incorrectly with other image segmentation methods, are correctly segmented and identified using this novel technology, which is available for licensing or co-development.

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.

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.