Emphasis
The emphasis is on using image data to achieve better health outcomes and smarter health care. Examples of technology development areas in this program include but are not limited to models, algorithms, software, methodologies, and other tools that will: facilitate medical imaging research; support clinical detection, diagnosis and therapy; and improve patient healthcare.
Program priorities and areas of interest:
- Image segmentation, image registration, atlas generation, image fusion, morphometry measurement, and the determination of function and structure from medical images
- Diagnostic-performance evaluation, computer-aided diagnosis, statistical models for evaluation of observer performance, and assessment of observer variability
- Quantitative imaging and image-based biomarkers
- Image-driven computer-aided diagnosis and decision support systems
- Virtual reality technologies
- Dose estimation and reduction software
Additional support
This program also supports:
- Early-stage validation of tools for image processing, visual perception and display
- Tools to assess image quality and observer performance
- Tools and software that enable large-scale, longitudinal and/or multi-site imaging studies and clinical trials
- Medical imaging mobile apps for early detection
Related News
A tiny, four-fingered “hand” folded from a single piece of DNA can pick up the virus that causes COVID-19 for highly sensitive rapid detection and can even block viral particles from entering cells to infect them, University of Illinois Urbana-Champaign researchers report. Source: University of Illinois Urbana-Champaign News Bureau
Due to its high accuracy, lab-based PCR testing is the gold standard for infectious disease diagnostics. Yet PCR's availability is limited, especially in low-resource settings. New research suggests a new kind of test could be more streamlined without sacrificing performance.
To date, nine medical device developers participating in the RADx® Tech Independent Test Assessment Program have received emergency use authorization for at-home and point-of care test products that simultaneously detect COVID-19 and flu A/B.
Scientists at the University of North Carolina at Chapel Hill have created innovative soft robots equipped with electronic skins and artificial muscles, allowing them to sense their surroundings and adapt their movements in real-time. These features make soft sensory robots highly adaptable and useful for enhancing medical diagnostics and treatments. Source: UNC Chapel Hill
NIH Blueprint MedTech program has issued nine awards in its first competition cycle. The program seeks to accelerate transformative medical devices to treat disorders of the nervous system.