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Artificial intelligence in radiology

The interest in artificial intelligence (AI) has ballooned within radiology in the past few years primarily due to notable successes of deep learning. With the advances brought by deep learning, AI has the potential to recognize and localize complex patterns from different radiological imaging modal...

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Autores principales: Jin, Dakai, Harrison, Adam P., Zhang, Ling, Yan, Ke, Wang, Yirui, Cai, Jinzheng, Miao, Shun, Lu, Le
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484814/
http://dx.doi.org/10.1016/B978-0-12-821259-2.00014-4
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author Jin, Dakai
Harrison, Adam P.
Zhang, Ling
Yan, Ke
Wang, Yirui
Cai, Jinzheng
Miao, Shun
Lu, Le
author_facet Jin, Dakai
Harrison, Adam P.
Zhang, Ling
Yan, Ke
Wang, Yirui
Cai, Jinzheng
Miao, Shun
Lu, Le
author_sort Jin, Dakai
collection PubMed
description The interest in artificial intelligence (AI) has ballooned within radiology in the past few years primarily due to notable successes of deep learning. With the advances brought by deep learning, AI has the potential to recognize and localize complex patterns from different radiological imaging modalities, many of which even achieve comparable performance to human decision-making in recent applications. In this chapter, we review several AI applications in radiology for different anatomies: chest, abdomen, pelvis, as well as general lesion detection/identification that is not limited to specific anatomies. For each anatomy site, we focus on introducing the tasks of detection, segmentation, and classification with an emphasis on describing the technology development pathway with the aim of providing the reader with an understanding of what AI can do in radiology and what still needs to be done for AI to better fit in radiology. Combining with our own research experience of AI in medicine, we elaborate how AI can enrich knowledge discovery, understanding, and decision-making in radiology, rather than replacing the radiologist.
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spelling pubmed-74848142020-09-11 Artificial intelligence in radiology Jin, Dakai Harrison, Adam P. Zhang, Ling Yan, Ke Wang, Yirui Cai, Jinzheng Miao, Shun Lu, Le Artificial Intelligence in Medicine Article The interest in artificial intelligence (AI) has ballooned within radiology in the past few years primarily due to notable successes of deep learning. With the advances brought by deep learning, AI has the potential to recognize and localize complex patterns from different radiological imaging modalities, many of which even achieve comparable performance to human decision-making in recent applications. In this chapter, we review several AI applications in radiology for different anatomies: chest, abdomen, pelvis, as well as general lesion detection/identification that is not limited to specific anatomies. For each anatomy site, we focus on introducing the tasks of detection, segmentation, and classification with an emphasis on describing the technology development pathway with the aim of providing the reader with an understanding of what AI can do in radiology and what still needs to be done for AI to better fit in radiology. Combining with our own research experience of AI in medicine, we elaborate how AI can enrich knowledge discovery, understanding, and decision-making in radiology, rather than replacing the radiologist. 2021 2020-09-11 /pmc/articles/PMC7484814/ http://dx.doi.org/10.1016/B978-0-12-821259-2.00014-4 Text en Copyright © 2021 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Jin, Dakai
Harrison, Adam P.
Zhang, Ling
Yan, Ke
Wang, Yirui
Cai, Jinzheng
Miao, Shun
Lu, Le
Artificial intelligence in radiology
title Artificial intelligence in radiology
title_full Artificial intelligence in radiology
title_fullStr Artificial intelligence in radiology
title_full_unstemmed Artificial intelligence in radiology
title_short Artificial intelligence in radiology
title_sort artificial intelligence in radiology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484814/
http://dx.doi.org/10.1016/B978-0-12-821259-2.00014-4
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