Artificial intelligence, deep learning to assist diagnosing birth defects

Canadian researchers have in a breakthrough experimented with the use of Artificial Intelligence-based deep learning as a tool for the early identification of birth defects.

A team from the University of Ottawa in a new proof-of-concept pioneered the use of a unique deep learning model as an assistive tool for the rapid and accurate reading of ultrasound images.

The goal of the study, published in the scientific journal Plos One, was to demonstrate the potential for deep-learning architecture to support early and reliable identification of cystic hygroma from first trimester ultrasound scans.

Cystic hygroma is an embryonic condition that causes the lymphatic vascular system to develop abnormally.