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Current status of deep learning applications in abdominal ultrasonography
Deep learning is one of the most popular artificial intelligence techniques used in the medical field. Although it is at an early stage compared to deep learning analyses of computed tomography or magnetic resonance imaging, studies applying deep learning to ultrasound imaging have been actively con...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Korean Society of Ultrasound in Medicine
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994733/ https://www.ncbi.nlm.nih.gov/pubmed/33242931 http://dx.doi.org/10.14366/usg.20085 |
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author | Song, Kyoung Doo |
author_facet | Song, Kyoung Doo |
author_sort | Song, Kyoung Doo |
collection | PubMed |
description | Deep learning is one of the most popular artificial intelligence techniques used in the medical field. Although it is at an early stage compared to deep learning analyses of computed tomography or magnetic resonance imaging, studies applying deep learning to ultrasound imaging have been actively conducted. This review analyzes recent studies that applied deep learning to ultrasound imaging of various abdominal organs and explains the challenges encountered in these applications. |
format | Online Article Text |
id | pubmed-7994733 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Korean Society of Ultrasound in Medicine |
record_format | MEDLINE/PubMed |
spelling | pubmed-79947332021-04-06 Current status of deep learning applications in abdominal ultrasonography Song, Kyoung Doo Ultrasonography Special Review of Artificial Intelligence (part 2) Deep learning is one of the most popular artificial intelligence techniques used in the medical field. Although it is at an early stage compared to deep learning analyses of computed tomography or magnetic resonance imaging, studies applying deep learning to ultrasound imaging have been actively conducted. This review analyzes recent studies that applied deep learning to ultrasound imaging of various abdominal organs and explains the challenges encountered in these applications. Korean Society of Ultrasound in Medicine 2021-04 2020-09-02 /pmc/articles/PMC7994733/ /pubmed/33242931 http://dx.doi.org/10.14366/usg.20085 Text en Copyright © 2021 Korean Society of Ultrasound in Medicine (KSUM) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Special Review of Artificial Intelligence (part 2) Song, Kyoung Doo Current status of deep learning applications in abdominal ultrasonography |
title | Current status of deep learning applications in abdominal ultrasonography |
title_full | Current status of deep learning applications in abdominal ultrasonography |
title_fullStr | Current status of deep learning applications in abdominal ultrasonography |
title_full_unstemmed | Current status of deep learning applications in abdominal ultrasonography |
title_short | Current status of deep learning applications in abdominal ultrasonography |
title_sort | current status of deep learning applications in abdominal ultrasonography |
topic | Special Review of Artificial Intelligence (part 2) |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994733/ https://www.ncbi.nlm.nih.gov/pubmed/33242931 http://dx.doi.org/10.14366/usg.20085 |
work_keys_str_mv | AT songkyoungdoo currentstatusofdeeplearningapplicationsinabdominalultrasonography |