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Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images

Several reviews have been conducted regarding artificial intelligence (AI) techniques to improve pregnancy outcomes. But they are not focusing on ultrasound images. This survey aims to explore how AI can assist with fetal growth monitoring via ultrasound image. We reported our findings using the gui...

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Autores principales: Alzubaidi, Mahmood, Agus, Marco, Alyafei, Khalid, Althelaya, Khaled A., Shah, Uzair, Abd-Alrazaq, Alaa, Anbar, Mohammed, Makhlouf, Michel, Househ, Mowafa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9287600/
https://www.ncbi.nlm.nih.gov/pubmed/35856024
http://dx.doi.org/10.1016/j.isci.2022.104713
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author Alzubaidi, Mahmood
Agus, Marco
Alyafei, Khalid
Althelaya, Khaled A.
Shah, Uzair
Abd-Alrazaq, Alaa
Anbar, Mohammed
Makhlouf, Michel
Househ, Mowafa
author_facet Alzubaidi, Mahmood
Agus, Marco
Alyafei, Khalid
Althelaya, Khaled A.
Shah, Uzair
Abd-Alrazaq, Alaa
Anbar, Mohammed
Makhlouf, Michel
Househ, Mowafa
author_sort Alzubaidi, Mahmood
collection PubMed
description Several reviews have been conducted regarding artificial intelligence (AI) techniques to improve pregnancy outcomes. But they are not focusing on ultrasound images. This survey aims to explore how AI can assist with fetal growth monitoring via ultrasound image. We reported our findings using the guidelines for PRISMA. We conducted a comprehensive search of eight bibliographic databases. Out of 1269 studies 107 are included. We found that 2D ultrasound images were more popular (88) than 3D and 4D ultrasound images (19). Classification is the most used method (42), followed by segmentation (31), classification integrated with segmentation (16) and other miscellaneous methods such as object-detection, regression, and reinforcement learning (18). The most common areas that gained traction within the pregnancy domain were the fetus head (43), fetus body (31), fetus heart (13), fetus abdomen (10), and the fetus face (10). This survey will promote the development of improved AI models for fetal clinical applications.
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spelling pubmed-92876002022-07-17 Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images Alzubaidi, Mahmood Agus, Marco Alyafei, Khalid Althelaya, Khaled A. Shah, Uzair Abd-Alrazaq, Alaa Anbar, Mohammed Makhlouf, Michel Househ, Mowafa iScience Article Several reviews have been conducted regarding artificial intelligence (AI) techniques to improve pregnancy outcomes. But they are not focusing on ultrasound images. This survey aims to explore how AI can assist with fetal growth monitoring via ultrasound image. We reported our findings using the guidelines for PRISMA. We conducted a comprehensive search of eight bibliographic databases. Out of 1269 studies 107 are included. We found that 2D ultrasound images were more popular (88) than 3D and 4D ultrasound images (19). Classification is the most used method (42), followed by segmentation (31), classification integrated with segmentation (16) and other miscellaneous methods such as object-detection, regression, and reinforcement learning (18). The most common areas that gained traction within the pregnancy domain were the fetus head (43), fetus body (31), fetus heart (13), fetus abdomen (10), and the fetus face (10). This survey will promote the development of improved AI models for fetal clinical applications. Elsevier 2022-07-03 /pmc/articles/PMC9287600/ /pubmed/35856024 http://dx.doi.org/10.1016/j.isci.2022.104713 Text en © 2022. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Alzubaidi, Mahmood
Agus, Marco
Alyafei, Khalid
Althelaya, Khaled A.
Shah, Uzair
Abd-Alrazaq, Alaa
Anbar, Mohammed
Makhlouf, Michel
Househ, Mowafa
Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images
title Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images
title_full Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images
title_fullStr Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images
title_full_unstemmed Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images
title_short Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images
title_sort toward deep observation: a systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9287600/
https://www.ncbi.nlm.nih.gov/pubmed/35856024
http://dx.doi.org/10.1016/j.isci.2022.104713
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