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Application and Progress of Artificial Intelligence in Fetal Ultrasound

Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of arti...

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Autores principales: Xiao, Sushan, Zhang, Junmin, Zhu, Ye, Zhang, Zisang, Cao, Haiyan, Xie, Mingxing, Zhang, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10179567/
https://www.ncbi.nlm.nih.gov/pubmed/37176738
http://dx.doi.org/10.3390/jcm12093298
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author Xiao, Sushan
Zhang, Junmin
Zhu, Ye
Zhang, Zisang
Cao, Haiyan
Xie, Mingxing
Zhang, Li
author_facet Xiao, Sushan
Zhang, Junmin
Zhu, Ye
Zhang, Zisang
Cao, Haiyan
Xie, Mingxing
Zhang, Li
author_sort Xiao, Sushan
collection PubMed
description Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician’s workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field.
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spelling pubmed-101795672023-05-13 Application and Progress of Artificial Intelligence in Fetal Ultrasound Xiao, Sushan Zhang, Junmin Zhu, Ye Zhang, Zisang Cao, Haiyan Xie, Mingxing Zhang, Li J Clin Med Review Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician’s workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field. MDPI 2023-05-05 /pmc/articles/PMC10179567/ /pubmed/37176738 http://dx.doi.org/10.3390/jcm12093298 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Xiao, Sushan
Zhang, Junmin
Zhu, Ye
Zhang, Zisang
Cao, Haiyan
Xie, Mingxing
Zhang, Li
Application and Progress of Artificial Intelligence in Fetal Ultrasound
title Application and Progress of Artificial Intelligence in Fetal Ultrasound
title_full Application and Progress of Artificial Intelligence in Fetal Ultrasound
title_fullStr Application and Progress of Artificial Intelligence in Fetal Ultrasound
title_full_unstemmed Application and Progress of Artificial Intelligence in Fetal Ultrasound
title_short Application and Progress of Artificial Intelligence in Fetal Ultrasound
title_sort application and progress of artificial intelligence in fetal ultrasound
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10179567/
https://www.ncbi.nlm.nih.gov/pubmed/37176738
http://dx.doi.org/10.3390/jcm12093298
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