Dr. Ambale-Venkatesh has been involved in image processing, machine learning, cardiovascular imaging and epidemiology research since 2004. His current research aims to help clinicians improve patient care by identifying new imaging markers to predict cardiovascular outcomes and assess the success of therapeutic interventions, and evaluating the pathophysiological mechanisms of cardiac diseases. He has been leading aspects of protocol development, data acquisition, image post-processing, imaging data curation and indexing, statistical analysis and conduction of multi-center trials as a co-investigator in the magnetic resonance imaging (MRI) and echocardiography core labs for National Institutes of Health (NIH)-sponsored large population-based studies. He has also taken the lead in designing and developing protocols for assessment of novel MRI-derived biomarkers of stem-cell therapy in those with peripheral arterial disease and myocardial infarction. Specifically, his research includes:
- Development of novel techniques for image analysis, data modeling, and image acquisition.
- Predicting adverse outcomes using machine learning in large multi-ethnic populations to generate preventive public health strategies.
- Using a combination of deep learning and advanced phenotyping to extract information from images and statistical machine learning to provide clinical insight.
Technology Expertise Keywords
MATLAB; Python; Keras; TensorFlow; PyTorch
View all on Pubmed
Ambale-Venkatesh B, Schiros CG, Himanshu Gupta, Lloyd SG, Dell'Italia L, Denney TS Jr. Three-dimensional plus time biventricular strain from tagged MR images by phase-unwrapped harmonic phase. Journal of Magnetic Resonance Imaging. 2011; 34(4): 799-810.
Ambale-Venkatesh B, Volpe GJ, Donekal S, Mewton N, Liu CY, Shea S, Liu K, Burke G, Wu CO, Bluemke DA, Lima JAC. Association of Longitudinal Changes in Left Ventricular Structure and Function with Myocardial Fibrosis: The MESA study. Hypertension. 2014; 64:508-515.
Ambale-Venkatesh B, Lima JAC. Cardiac MRI: a central prognostic tool in myocardial fibrosis. Nature Reviews Cardiology 12 (1), 18-29, 2015.
Ambale-Venkatesh B, Nauffal V, Noda C, Fujii T, Yang PC, Bettencourt J, Ricketts EP, Murphy MP, Leeper NJ, Moye L, Ebert RF, Muthupillai R, Bluemke DA, Perin EC, Hirsch AT, Lima JAC, Cardiovascular Cell Therapy Research Network (CCTRN). Baseline assessment and comparison of arterial anatomy, hyperemic flow, and skeletal muscle perfusion in peripheral artery disease: The Cardiovascular Cell Therapy Research Network - Patients with Intermittent Claudication Injected with ALDH Bright Cells (CCTRN PACE) study. American Heart Journal, Vol 183, January 2017.
Ambale-Venkatesh B, Yang X, Wu CO, Liu K, WG Hundley, McClelland RL, Gomes SA, Folsom AR, Shea S, Guallar E, Bluemke DA, Lima JAC. Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis. Circ Res. 2017 Oct 13;121(9):1092-1101.