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Abstract #1849

Feasibility of machine learning for cardiovascular function analysis in patients with repaired tetralogy of Fallot

Elizabeth Walker Thompson1, Abhijit Bhattaru1, Phuong Vu1, Elizabeth Donnelly2, Elizabeth Goldmuntz2, Mark Fogel2, and Walter Witschey1
1University of Pennsylvania, Philadelphia, PA, United States, 2Children's Hospital of Philadelphia, Philadelphia, PA, United States


Tetralogy of Fallot (ToF) is a congenital heart disease that is typically repaired with surgery early in life, but right ventricular remodeling results in adverse events for many patients. This preliminary analysis of 8 patients investigated the feasibility of training a convolutional neural network to segment the right and left ventricles from 2-dimensional cardiovascular magnetic resonance images, resulting in Dice scores ranging from 0.73-0.91 for the left ventricular blood pool, left ventricular myocardium, and right ventricular blood pool. Machine learning shows promise to enable large-scale longitudinal studies of ToF.

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