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

Grant Number Project Title Principal Investigator Institution
5-R21-EB033455-03 Knowledge-informed Deep Learning for Apnea Detection with Limited Annotations Xiaochen Xian University of Florida
1-R21-EB030294-01A1 Machine learning approach to non-invasive MRI-based blood oximetry Juliet Varghese Ohio State University
5-R21-EB030762-02 Machine Learning-Based Adaptation of Data Sampling and Reconstruction for Efficient Dynamic MRI Saiprasad Ravishankar Michigan State University
4-R00-EB033857-03 Machine Learning-enabled Classification of Extracellular Vesicles Using Nanoplasmonic Microfluidics Colin Hisey Northwestern University
5-R01-EB033788-02 Maternal mHealth blood hemoglobin analysis with informed deep learning Young Kim Purdue University
5-R01-EB029944-04 MRI and Deep Learning for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants Lili He Cincinnati Childrens Hosp Med Ctr
5-R01-EB031032-04 Non-invasive automated wound analysis via deep learning neural networks Kyle Quinn University of Arkansas at Fayetteville
1-R01-EB035394-01A1 Optimizing Mobile Photon-Counting CT Image Quality via Deep Learning for Neuro Intensive Care Unit Dufan Wu Massachusetts General Hospital
5-R01-EB022573-08 Personalized Functional Network Modeling to Characterize and Predict Psychopathology in Youth Yong Fan University of Pennsylvania
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