Artificial intelligence (AI) deep learning is rapidly emerging as an innovative diagnostic tool for life sciences and health care. A new study demonstrates how AI deep learning can be used to diagnose a rare embryonic developmental disorder called cystic hygroma within the first trimester of pregnancy from fetal ultrasound images. "In this proof-of-concept study, we demonstrate the potential for deep-learning to support early and reliable identification of cystic hygroma from first trimester ultrasound scans," wrote Dr. Mark Walker MD, FRCSC, MSc, MHCM, at the University of Ottawa (uOttawa) Faculty of Medicine and his research team. Dr. Walker is a perinatologist, a clinical epidemiologist, high-risk obstetrician, co-founder of the OMNI Research Group (Obstetrics, Maternal and Newborn Investigations) at The Ottawa Hospital, which is the largest maternal and newborn research group in Canada, and a professor and the Vice-Dean of Internationalization and Global Health at the uOttawa Faculty of Medicine. He has published over 160 peer reviewed articles.

Summary: Using datasets of fetal ultrasounds, a new AI algorithm is able to detect cystic hygroma, a rare embryonic developmental disorder, within the first trimester of pregnancy. In a new proof-of-concept study led by Dr. Mark Walker at the uOttawa Faculty of Medicine, researchers are pioneering the use of a unique AI-based deep learning model as an assistive tool for the rapid and accurate reading of ultrasound images. It's trailblazing work because although deep learning models have become increasingly popular in interpreting medical images and detecting disorders, figuring out how its application can work in obstetric ultrasonography is in its nascent stages. Few AI-enabled studies have been published in this field. The goal of the team's study was to demonstrate the potential for deep-learning architecture to support early and reliable identification of cystic hygroma from first trimester ultrasound scans.