In a groundbreaking experiment, Canadian researchers used Artificial Intelligence-based deep learning as a tool for the early detection of birth defects.
In a new proof-of-concept study, a team from the University of Ottawa pioneered the use of a novel deep learning model as an assistive tool for the rapid and accurate reading of ultrasound images.
The study’s goal, which was published in the scientific journal Plos One, was to show that deep-learning architecture has the potential to support early and reliable identification of cystic hygroma from first trimester ultrasound scans.
Cystic hygroma is an embryonic condition that causes an abnormal development of the lymphatic vascular system. It is a rare and potentially fatal disorder characterised by fluid swelling around the head and neck. It has been documented in approximately one in every 800 pregnancies and one in every 8,000 live births.
Although ultrasound is essential for observing foetal growth and development, small foetal structures, involuntary foetal movements, and poor image quality make neonatal image acquisition and interpretation difficult. The research team wanted to see how well AI-powered pattern recognition could perform.
“What we demonstrated was that in the field of ultrasound we’re able to use the same tools for image classification and identification with a high sensitivity and specificity,” said Dr Mark Walker at the University’s Faculty of Medicine.
“With further development, including testing in a large multi-site dataset and external validation, our approach may be applied to a range of other foetal anomalies typically identified by ultrasonography,” he noted.
