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Application and prospects of AI-based radiomics in ultrasound diagnosis
Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imag...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570254/ https://www.ncbi.nlm.nih.gov/pubmed/37828411 http://dx.doi.org/10.1186/s42492-023-00147-2 |
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author | Zhang, Haoyan Meng, Zheling Ru, Jinyu Meng, Yaqing Wang, Kun |
author_facet | Zhang, Haoyan Meng, Zheling Ru, Jinyu Meng, Yaqing Wang, Kun |
author_sort | Zhang, Haoyan |
collection | PubMed |
description | Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis. |
format | Online Article Text |
id | pubmed-10570254 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-105702542023-10-14 Application and prospects of AI-based radiomics in ultrasound diagnosis Zhang, Haoyan Meng, Zheling Ru, Jinyu Meng, Yaqing Wang, Kun Vis Comput Ind Biomed Art Review Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis. Springer Nature Singapore 2023-10-13 /pmc/articles/PMC10570254/ /pubmed/37828411 http://dx.doi.org/10.1186/s42492-023-00147-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Zhang, Haoyan Meng, Zheling Ru, Jinyu Meng, Yaqing Wang, Kun Application and prospects of AI-based radiomics in ultrasound diagnosis |
title | Application and prospects of AI-based radiomics in ultrasound diagnosis |
title_full | Application and prospects of AI-based radiomics in ultrasound diagnosis |
title_fullStr | Application and prospects of AI-based radiomics in ultrasound diagnosis |
title_full_unstemmed | Application and prospects of AI-based radiomics in ultrasound diagnosis |
title_short | Application and prospects of AI-based radiomics in ultrasound diagnosis |
title_sort | application and prospects of ai-based radiomics in ultrasound diagnosis |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570254/ https://www.ncbi.nlm.nih.gov/pubmed/37828411 http://dx.doi.org/10.1186/s42492-023-00147-2 |
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