UNC Hospitals – Chapel Hill
Education and Training:
PhD, Computer Science, Institut National des Science Appliquées
Dr. Prieto is a software developer with 9+ years of experience. His current work with Dr. Jeff Stringer in Obstetrics and Gynecology involves the Ultrasound Fetal Age Machine Learning Initiative (US-FAMLI) which aims to develop a low-cost obstetric Ultrasound (US) for use in low- and middle-income countries (LMICs) that uses machine-learning algorithms to estimate gestational age and make diagnoses without requiring a skilled ultrasonographer. US-FAMLI has access to US images from the University of Zambia and UNC Hospital. An accurate gestational age system gives insight into fetal well-being and pathologies such as growth restriction or macrosomia, as well as identifying an infant’s needs in feeding, sleep, and nursing care or interactive behaviors with the parents. It would help planning for prenatal screening tests for aneuploidy, reduce post-dates labor induction, and allow timing of necessary interventions and avoidance of unnecessary ones.
Dr. Prieto also uses fiber tract classification using deep learning (TRAFIC) which is a fully automated tool for the labeling and classification of brain fiber tracts. TRAFIC classifies new fibers using a neural network trained to use shape features computed from previously traced and manually corrected fiber tracts. This work is motivated by medical applications where the process of extracting fibers from a DTI atlas or classifying fibers manually is time-consuming and requires knowledge about brain anatomy. With this new approach, they were able to classify traced fiber tracts obtaining encouraging results. They hope to accelerate the process and avoid manually correcting the fiber tracts.