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Artificial intelligence in breast ultrasound
Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imagin...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6403465/ https://www.ncbi.nlm.nih.gov/pubmed/30858931 http://dx.doi.org/10.4329/wjr.v11.i2.19 |
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author | Wu, Ge-Ge Zhou, Li-Qiang Xu, Jian-Wei Wang, Jia-Yu Wei, Qi Deng, You-Bin Cui, Xin-Wu Dietrich, Christoph F |
author_facet | Wu, Ge-Ge Zhou, Li-Qiang Xu, Jian-Wei Wang, Jia-Yu Wei, Qi Deng, You-Bin Cui, Xin-Wu Dietrich, Christoph F |
author_sort | Wu, Ge-Ge |
collection | PubMed |
description | Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women’s health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound. |
format | Online Article Text |
id | pubmed-6403465 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-64034652019-03-11 Artificial intelligence in breast ultrasound Wu, Ge-Ge Zhou, Li-Qiang Xu, Jian-Wei Wang, Jia-Yu Wei, Qi Deng, You-Bin Cui, Xin-Wu Dietrich, Christoph F World J Radiol Minireviews Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women’s health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound. Baishideng Publishing Group Inc 2019-02-28 2019-02-28 /pmc/articles/PMC6403465/ /pubmed/30858931 http://dx.doi.org/10.4329/wjr.v11.i2.19 Text en ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. |
spellingShingle | Minireviews Wu, Ge-Ge Zhou, Li-Qiang Xu, Jian-Wei Wang, Jia-Yu Wei, Qi Deng, You-Bin Cui, Xin-Wu Dietrich, Christoph F Artificial intelligence in breast ultrasound |
title | Artificial intelligence in breast ultrasound |
title_full | Artificial intelligence in breast ultrasound |
title_fullStr | Artificial intelligence in breast ultrasound |
title_full_unstemmed | Artificial intelligence in breast ultrasound |
title_short | Artificial intelligence in breast ultrasound |
title_sort | artificial intelligence in breast ultrasound |
topic | Minireviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6403465/ https://www.ncbi.nlm.nih.gov/pubmed/30858931 http://dx.doi.org/10.4329/wjr.v11.i2.19 |
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