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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...

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Detalles Bibliográficos
Autores principales: Wu, Ge-Ge, Zhou, Li-Qiang, Xu, Jian-Wei, Wang, Jia-Yu, Wei, Qi, Deng, You-Bin, Cui, Xin-Wu, Dietrich, Christoph F
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2019
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.
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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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