Use of deep-learning architecture to identify rare, life-threatening disorders from fetal ultrasound scans

In a new proof-of-concept study led by Dr. Mark Walker of the University of Ottawa’s Faculty of Medicine, researchers are pioneers in the use of a unique artificial intelligence-based deep learning model as an aid to fast and accurate reading of ultrasound images.

The goal of the team study was to demonstrate the potential of 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. It is a rare and potentially life-threatening disorder that leads to fluid swelling around the head and neck.

The birth defect can typically be easily diagnosed prenatally during an ultrasound interview, but Dr. Walker – co-founder of the OMNI Research Group (Obstetrics, Maternal and Newborn Investigations) at Ottawa Hospital – and his research team wanted to test how well AI-driven pattern recognition could do the job.

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


Journal reference:

10.1371 / journal.pone.0269323

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