Abstract
Fetal development is a complex physiological process involving multiple maternal, placental, and environmental factors. Alterations in these processes—such as in the circulatory system, maternal malnutrition, infections, systemic inflammation, or placental dysfunction—can lead to neonates who are small for gestational age, preterm, or low birth weight, with substantial consequences for neonatal health and long-term development. Despite advances in fetal biometry and Doppler for assessing fetal growth and detecting signs of placental dysfunction, most diagnostic approaches rely on single measurements and classical statistical models, without exploiting the multifactorial and longitudinal nature of pregnancy. The AGE-US method proposes a machine learning approach to estimate gestational age from fetal ultrasound images, aiming for more accurate, automated, and reproducible estimations.
César Díaz Parga
PhD student
My research interests include deep learning strategies, generative models, and biometrical inference in fetal ultrasound.
Nicolás Vila Blanco
Assistant Professor Doctor
My research interests include medical image processing and multimodal learning.