AIMI Journal Club: Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning & Machine Learning Integration of Circulating and Imaging Biomarkers for Explainable Patient-Specific Prediction of Cardiac Events - Damini Dey, PhD

Stanford community & AIMI affiliates only - recording will be available to the public after the event

Papers:

• Commandeur F, Goeller M, Razipour A, Cadet S, Hell MM, Kwiecinski J, Chen X, Chang HJ, Marwan M, Achenbach S, Berman DS, Slomka PJ, Tamarappoo BK, Dey D. Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter Study. Radiol Artif Intell. 2019 Nov 27;1(6):e190045. doi: 10.1148/ryai.2019190045. PMID: 32090206; PMCID: PMC6884062.
• Tamarappoo BK, Lin A, Commandeur F, McElhinney PA, Cadet S, Goeller M, Razipour A, Chen X, Gransar H, Cantu S, Miller RJ, Achenbach S, Friedman J, Hayes S, Thomson L, Wong ND, Rozanski A, Slomka PJ, Berman DS, Dey D. Machine learning integration of circulating and imaging biomarkers for explainable patient-specific prediction of cardiac events: A prospective study. Atherosclerosis. 2021 Feb;318:76-82. doi: 10.1016/j.atherosclerosis.2020.11.008. Epub 2020 Nov 13. PMID: 33239189; PMCID: PMC7856265.

About:
Damini Dey, PhD is a Professor and Research Scientist at the Biomedical Imaging Research Institute, Department of Biomedical Sciences at the Cedars-Sinai Medical Center. Dr. Dey is the director of the Quantitative Image Analysis Program and technical co-director of PET-MR at the Biomedical Imaging Research Institute, and Professor at the David Geffen School of Medicine at University of California, Los Angeles.

Dr. Dey received her doctorate in medical physics from the University of Calgary in Canada. Her expertise is in quantitative cardiac imaging and in cardiac CT and PET. Her research investigations include automated quantitative analysis and characterization of coronary plaque from coronary CT Angiography, automated quantitative measurement of epicardial and thoracic fat from cardiac CT, artificial intelligence in cardiovascular imaging and machine learning integration of imaging biomarkers towards precision medicine, and improvement of cardiac PET-MR imaging.