The NIH Common Fund’s Bridge to Artificial Intelligence, or Bridge2AI, program will help set the stage for widespread use of artificial intelligence to solve some of the most pressing challenges in human health. The program will bring together research team members from richly diverse backgrounds and disciplines to generate ethically sourced tools, data and resources, and ultimately bridge the gap between biomedical and behavioral research and artificial intelligence.
Bridge2AI is a trans-NIH program funded by the NIH Common Fund that is collaboratively overseen by the NIH Common Fund, the National Center for Complementary and Integrative Health, the National Eye Institute, the National Human Genome Research Institute, National Institute of Biomedical Imaging and Bioengineering, and the National Library of Medicine.
Two University of Alabama at Birmingham-led research teams out of approximately 100 contesting teams nationwide have been selected for funding by this program. One will be led by the Department of Ophthalmology and Visual Sciences and the other by researchers in the UAB Informatics Institute.
The UAB Department of Ophthalmology and Visual Sciences team have received a one-year, $2 million grant from the NIH’s Bridge2AI Program (OT2OD032644). The program is funded on a year-by-year basis for each of four years. The grant is based at the University of Washington, whose total first-year award is $7.8 million.
UAB represents one module called Data Acquisition, the largest of the six-module award led by the University of Washington.
Cynthia Owsley, Ph.D., professor and director of the Clinical Research Unit within the UAB Marnix E. Heersink School of Medicine’s Department of Ophthalmology and Visual Sciences, and Gerald McGwin Jr., Ph.D., professor with the UAB School of Public Health, together with Jeffrey Edberg, Ph.D., professor in the UAB Department of Medicine, will oversee creating flagship data sets. These data sets will be based on ethical principles, associated standards and tools, and skills and workforce development. The focus is to address biomedical and behavioral research grand challenges that require artificial and machine learning analysis.
“Our award is to lead three sites in the collection of data that will be used for machine learning approaches that will provide critical insights into the endemic condition Type 2 diabetes mellitus,” Owsley said.
“Our approach is to collect data on 4,000 adults in three areas of the United States with equal representation of four racial/ethnic groups and stages of Type 2 diabetes severity,” Owsley said. “Building balanced training data sets is critical for the development of unbiased AI/ML models. Thus, rather than targeting the demographic distribution of the U.S. population, we intentionally will recruit equal numbers of four racial/ethnic groups — Blacks, whites, and those of Asian origin and those of Hispanic origin.”
Owsley adds that the same rationale applies for balancing diabetic severity, from pre-diabetes, lifestyle-controlled, oral medication-controlled and insulin-controlled diabetes. AI/ML ready data will include social determinants of health, continuous glucose monitoring, serological testing for endocrine, cardiac and renal biomarkers, genome-wide polymorphism assessment, vision and retinal imaging, cognitive testing, EKG, and 24-hour activity monitoring.
Data collection sites will be at UAB, University of California San Diego and University of Washington. UAB’s Center for Clinical and Translational Science’s Biospecimen Repository will oversee biospecimen samples for all sites.
Original source can be found here.
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