Pediatric Heart Disease Diagnosis

Maria Escobar
Angela Castillo
Pablo Arbeláez

In the framework of “DELFOS: Diagnóstico, entrenamiento, logística, formación, organización y seguimiento de pacientes con cardiopatías congénitas”
Collaboration with Juan Carlos Briceño, Mauricio Herrera, Camila Castro, Gabriel Herrera, Rodolfo Dennis, Nestor Sandoval, Karen Moreno, Pablo Sandoval, Alberto García, Miguel Ronderos, Ignacio Zarante, Jaime Silva,Paula Hurtado, Dario Londoño, Alejandra Taborda, Cindy Chamorro, Daniel Afanador, Nicolás Niño, Milena Prada and Juan Quintero.
funded by MinCiencias


Congenital heart defects are changes in the function and shape of the heart that are developed during cardiac embryogenesis and are evident at birth. During prenatal ultrasounds, the OB-GYN doctor should completely review the fetal heart to determine if there is any malformation in it. In this way, when having suspicion of a heart defect, it is expected to be treated before delivery or at birth time to increase the baby’s life chance. However, congenital heart defects are not detected efficiently, which is why they cause 24% of deaths in the first year of infants’ life.

For this reason, we propose an Artificial Intelligence algorithm that accompanies doctors in the diagnosis of congenital heart defects. This project is developed within the framework of the program "DELFOS: Diagnosis, training, logistics, education, organization and monitoring of patients with congenital heart defects" and is in charge of integrating the data obtained in the stage of congenital heart defects follow-up and training of screening centers.

Presentation Video

Publications

UltraGAN: Ultrasound Enhancement through Adversarial Generation

M. Escobar*, A. Castillo*, A. Romero, P. Arbeláez

Workshop on Simulation and Synthesis in Medical Imaging (SASHIMI) 2020

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