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Using AI to diagnose birth defect in fetal ultrasound images

Date:
July 14, 2022
Source:
University of Ottawa
Summary:
In a new proof-of-concept study researchers are pioneering the use of a unique Artificial Intelligence-based deep learning model as an assistive tool for the rapid and accurate reading of ultrasound images.
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In a new proof-of-concept study led by Dr. Mark Walker at the University of Ottawa's Faculty of Medicine, researchers are pioneering the use of a unique Artificial Intelligence-based deep learning model as an assistive tool for the rapid and accurate reading of ultrasound images.

The goal of the team's study 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. It's 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 appointment, but Dr. Walker -- co-founder of the OMNI Research Group (Obstetrics, Maternal and Newborn Investigations) at The Ottawa Hospital -- and his research group wanted to test how well AI-driven pattern recognition could do the job.

"What we demonstrated was 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.

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Story Source:

Materialsprovided byUniversity of Ottawa.注意:内容可能被编辑风格d length.


Journal Reference:

  1. Mark C. Walker, Inbal Willner, Olivier X. Miguel, Malia S. Q. Murphy, Darine El-Chaâr, Felipe Moretti, Alysha L. J. Dingwall Harvey, Ruth Rennicks White, Katherine A. Muldoon, André M. Carrington, Steven Hawken, Richard I. Aviv.Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester.PLOS ONE, 2022; 17 (6): e0269323 DOI:10.1371/journal.pone.0269323

Cite This Page:

University of Ottawa. "Using AI to diagnose birth defect in fetal ultrasound images." ScienceDaily. ScienceDaily, 14 July 2022. /releases/2022/07/220714145147.htm>.
University of Ottawa. (2022, July 14). Using AI to diagnose birth defect in fetal ultrasound images.ScienceDaily. Retrieved August 15, 2023 from www.koonmotors.com/releases/2022/07/220714145147.htm
University of Ottawa. "Using AI to diagnose birth defect in fetal ultrasound images." ScienceDaily. www.koonmotors.com/releases/2022/07/220714145147.htm (accessed August 15, 2023).

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