PROJECT SUMMARY
Vineet Raghu, PhD is a computer scientist whose career goal is to improve early detection and prevention of
chronic disease by applying artificial intelligence to large datasets of medical imaging, genomics, risk factors,
and outcomes to better estimate risk and derive insight into etiology. Dr. Raghu is an Instructor of Radiology at
Harvard Medical School and research faculty at the Massachusetts General Hospital’s Cardiovascular Imaging
Research Center. In this project, he aims to develop and validate deep learning-based cardiovascular disease
risk scores from chest imaging. He will build upon his prior training in applied artificial intelligence and
genomics to gain depth of understanding in the epidemiology, pathophysiology, and preventive care of
cardiovascular disease. His specific training goals are to: 1) Gain understanding of how physicians and
patients make decisions about preventive interventions in older adults, 2) Learn to apply statistical genetics
techniques to single nucleotide polymorphism and epigenetic data to investigate molecular pathways of
cardiovascular disease, and 3) Gain deeper understanding of the pathophysiology of cardiovascular disease to
improve collaboration with physician-scientists, and 4) Develop grant writing skills and training in the
responsible conduct of research. Dr. Raghu’s training plan includes didactic training from Harvard Medical
School and Harvard School of Public Health and hands-on research training to gain the proposed skillset in
preparation for research independence. His mentorship committee has diverse expertise in imaging, deep
learning, genetics, geriatrics, and cardiovascular epidemiology to support his development. He and his
mentorship team will have individual meetings to discuss specific research aims and advisory meetings to
discuss broader career development goals. Dr. Raghu will supplement didactic training by completing the
following innovative research aims. In Aim 1, he will develop deep learning models to estimate cardiovascular
disease risk using chest CT. In Aim 2, he will identify genetic loci and biologic aging indices associated with
CT-based risk estimates. Finally, in Aim 3, he will test whether CT-based risk estimates can identify persons at
risk for incident cardiovascular events beyond current risk scores and screening criteria. These aims will be
carried out in existing large epidemiologic cohorts comprising over 70,000 individuals in total. Completion of
these aims will provide preliminary data for a potential R01 application in Years 4 and 5 to investigate whether
CT-based models improve uptake and efficacy of preventive care and reduce cardiovascular events, to expand
this approach to other imaging modalities, or to use imaging and multi-omics to identify molecular pathways of
disease. This proposal will give Dr. Raghu excellent training to achieve his goal of being an independent
investigator who develops deep learning-based risk scores and 1) collaborates with physicians to implement
such models to improve preventive care and 2) uses such models to better understand disease etiology.