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Funded Projects for Artificial Intelligence, Machine Learning, and Deep Learning

Grant Number Project Title Principal Investigator Institution
5-R01-EB031032-04 Non-invasive automated wound analysis via deep learning neural networks Kyle Quinn University of Arkansas at Fayetteville
1-R01-EB036037-01A1 Optimization and Validation of an AI Model that Screens for Arteriovenous Fistula Stenosis in Dialysis Patients using Sound Files from a Digital Stethoscope Bobak Mosadegh Weill Medical Coll of Cornell Univ
5-R01-EB035394-02 Optimizing Mobile Photon-Counting CT Image Quality via Deep Learning for Neuro Intensive Care Unit Dufan Wu Massachusetts General Hospital
7-R01-EB035394-03 Optimizing Mobile Photon-Counting CT Image Quality via Deep Learning for Neuro Intensive Care Unit Dufan Wu Ohio State University
5-R01-EB022573-08 Personalized Functional Network Modeling to Characterize and Predict Psychopathology in Youth Yong Fan University of Pennsylvania
1-R01-EB036579-01A1 Pragmatic and automated pressure injury detection across a heterogeneous patient population Sharon Sonenblum Emory University
1-R21-EB034428-01A1 Predicting recovery after TBI: Development and comparison of MR-supplemented models using non-parametric and machine learning multimodal fusion Martin Monti University of California Los Angeles
5-R01-EB030582-04 Quantification of Liver Fibrosis with MRI and Deep Learning Lili He Cincinnati Childrens Hosp Med Ctr
2-R01-EB020683-05A1 Quantitative imaging and molecular data modeling for brain tumor recurrence and progression analysis Khan Iftekharuddin Old Dominion University
1-R01-EB036013-01A1 Resolution Enhancement and Contrast Harmonization for MR Neuroimaging Jerry Prince Johns Hopkins University