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Artificial intelligence in medical imaging of the liver
Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis...
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/PMC6378542/ https://www.ncbi.nlm.nih.gov/pubmed/30783371 http://dx.doi.org/10.3748/wjg.v25.i6.672 |
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author | Zhou, Li-Qiang Wang, Jia-Yu Yu, Song-Yuan Wu, Ge-Ge Wei, Qi Deng, You-Bin Wu, Xing-Long Cui, Xin-Wu Dietrich, Christoph F |
author_facet | Zhou, Li-Qiang Wang, Jia-Yu Yu, Song-Yuan Wu, Ge-Ge Wei, Qi Deng, You-Bin Wu, Xing-Long Cui, Xin-Wu Dietrich, Christoph F |
author_sort | Zhou, Li-Qiang |
collection | PubMed |
description | Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians’ workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques. |
format | Online Article Text |
id | pubmed-6378542 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-63785422019-02-19 Artificial intelligence in medical imaging of the liver Zhou, Li-Qiang Wang, Jia-Yu Yu, Song-Yuan Wu, Ge-Ge Wei, Qi Deng, You-Bin Wu, Xing-Long Cui, Xin-Wu Dietrich, Christoph F World J Gastroenterol Minireviews Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians’ workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques. Baishideng Publishing Group Inc 2019-02-14 2019-02-14 /pmc/articles/PMC6378542/ /pubmed/30783371 http://dx.doi.org/10.3748/wjg.v25.i6.672 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 Zhou, Li-Qiang Wang, Jia-Yu Yu, Song-Yuan Wu, Ge-Ge Wei, Qi Deng, You-Bin Wu, Xing-Long Cui, Xin-Wu Dietrich, Christoph F Artificial intelligence in medical imaging of the liver |
title | Artificial intelligence in medical imaging of the liver |
title_full | Artificial intelligence in medical imaging of the liver |
title_fullStr | Artificial intelligence in medical imaging of the liver |
title_full_unstemmed | Artificial intelligence in medical imaging of the liver |
title_short | Artificial intelligence in medical imaging of the liver |
title_sort | artificial intelligence in medical imaging of the liver |
topic | Minireviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6378542/ https://www.ncbi.nlm.nih.gov/pubmed/30783371 http://dx.doi.org/10.3748/wjg.v25.i6.672 |
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