301-451-6771
shabestb@mail.nih.gov
Emphasis
The emphasis is on development of transformative machine intelligence-based systems, emerging tools, and modern technologies for diagnosing and recommending treatments for a range of diseases and health conditions. Unsupervised and semi-supervised techniques and methodologies are of particular interest.
Program priorities and areas of interest:
- clinical decision support systems
- computer-aided diagnosis
- computer-aided screening
- analyzing complex patterns and images
- screening for diseases
- natural-language processing and understanding
- medical decision-making
- predictive modeling
- computer vision
- robotic and image guided surgery
- personalized imaging and treatment
- drug discovery
- radiomics
- machine/deep learning-based segmentation, registration, etc.
Additional support
This program also supports:
- early-stage development of software, tools, and reusable convolutional neural networks
- data reduction, denoising, improving performance (health-promoting apps), and deep-learning based direct image reconstruction
- approaches that facilitate interoperability among annotations used in image training databases
Related News
A team of engineers at the University of Houston has published a study in the journal Nature on how international air travel has influenced the spread of COVID-19 around the world. By using a newly developed AI tool, the team identified hotspots of infection linked to air traffic, pinpointing key areas that significantly contribute to disease transmission. Source: University of Houston Newsroom
Researchers at Washington University Medicine have reduced scar formation and improved heart function in mouse models of heart failure using a monoclonal antibody treatment. The antibody that reduces inflammation could serve as cardio-immunotherapy for heart failure patients. Source: WashU Medicine
The University of Chicago Pritzker School of Molecular Engineering (PME) has solved a challenge that has long stymied researchers, reimagining the process of creating hydrogels to build a powerful semiconductor in hydrogel form that can be used to create better brain-machine interfaces, biosensors, and pacemakers. Source: UChicago Pritzker School of Molecular Engineering News.
NIBIB bioengineer Kaitlyn Sadtler has flourished as a leader of many impactful, interdisciplinary studies. For her role in shaping the future of medical research, TIME magazine has named Kaitlyn Sadtler to the TIME100 Next 2024 List.
A team of researchers at Penn State College of Medicine and collaborators from five institutes have developed a new 3D atlas of developing mice brains using advanced imaging and microscopy techniques. The new high-resolution maps of the mouse brain will help advance the understanding of brain development and the study of neurodevelopment disorders.
Source: Penn State Research News