Using AI to Diagnose Birth Defect in Fetal Ul

0

image: Lead author Dr. Mark Walker of the University of Ottawa Faculty of Medicine is also a co-founder of the OMNI (Obstetrics, Maternal and Newborn Investigations) Research Group at The Ottawa Hospital.
see After

Credit: Faculty of Medicine, University of Ottawa

In a new proof-of-concept study led by Dr. Mark Walker of the University of Ottawa’s Faculty of Medicine, researchers are at the forefront of using a unique deep learning model based on artificial intelligence as a support tool for fast and accurate reading of ultrasound images.

The goal of the team’s study was to demonstrate the potential of a deep learning architecture to support the early and reliable identification of cystic hygroma from first trimester ultrasounds. Cystic hygroma is an embryonic condition that causes abnormal development of the lymphatic vascular system. It is a rare and life-threatening condition that causes swelling of fluid around the head and neck.

The birth defect can usually be easily diagnosed before birth during an ultrasound, but Dr. Walker – co-founder of the OMNI (Obstetrics, Maternal and Newborn Surveys) research group at The Ottawa Hospital – and his research wanted to test the quality of AI – driven pattern recognition might do the trick.

“What we have demonstrated is that in the field of ultrasound, we are able to use the same tools for image classification and identification with high sensitivity and specificity,” says Dr. Walker, who thinks their approach could be applied to other commonly identified fetal anomalies. by ultrasound.

The results were recently published in PLOS ONE, a peer-reviewed open access journal. Read full details of the Faculty of Medicine here.


Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of press releases posted on EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Share.

Comments are closed.