Researchers at the University of Ottawa 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.

The aim was to demonstrate the potential for deep-learning architecture to support early and reliable identification of cystic hygroma from first trimester ultrasound scans.

The birth defect can typically be easily diagnosed prenatally during an ultrasound appointment, but Dr. Mark Walker, co-founder of the OMNI Research Group (Obstetrics, Maternal and Newborn Investigations) at The Ottawa Hospital, wanted to test how well AI-driven pattern recognition could do the job. “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,” says Dr. Walker, who believes their approach might be applied to other fetal anomalies generally identified by ultrasonography.

The findings were recently published in PLOS ONE, a peer-reviewed open access journal.

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