Use of artificial intelligence and deep learning in fetal ultrasound imaging

• Published in: Ultrasound in obstetrics & gynecology Vol. 62; no. 2; pp. 185 - 194
• Main Authors: Ramirez Zegarra, R., Ghi, T.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01-08-2023; Wiley Subscription Services, Inc
• Subjects: Algorithms; Artificial Intelligence; Deep Learning; Female; Fetuses; Gynecology; Humans; Image analysis; Image processing; Machine learning; Medical imaging; Obstetrics; Pregnancy; Prenatal Diagnosis; Ultrasonography; Ultrasonography, Prenatal - methods; Ultrasound
• ISSN: 0960-7692; 1469-0705
• DOI: 10.1002/uog.26130

ABSTRACT
Deep learning is considered the leading artificial intelligence tool in image analysis in general. Deep‐learning algorithms excel at image recognition, which makes them valuable in medical imaging. Obstetric ultrasound has become the gold standard imaging modality for detection and diagnosis of fetal malformations. However, ultrasound relies heavily on the operator's experience, making it unreliable in inexperienced hands. Several studies have proposed the use of deep‐learning models as a tool to support sonographers, in an attempt to overcome these problems inherent to ultrasound. Deep learning has many clinical applications in the field of fetal imaging, including identification of normal and abnormal fetal anatomy and measurement of fetal biometry. In this Review, we provide a comprehensive explanation of the fundamentals of deep learning in fetal imaging, with particular focus on its clinical applicability. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.