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유방촬영술에서 인공지능의 적용: 알고리즘 개발 및 평가 관점
Mammography is the primary imaging modality for breast cancer detection; however, a high level of expertise is needed for its interpretation. To overcome this difficulty, artificial intelligence (AI) algorithms for breast cancer detection have recently been investigated. In this review, we describe...
Formato: | Online Artículo Texto |
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Lenguaje: | English |
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The Korean Society of Radiology
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9432399/ https://www.ncbi.nlm.nih.gov/pubmed/36237466 http://dx.doi.org/10.3348/jksr.2020.0205 |
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collection | PubMed |
description | Mammography is the primary imaging modality for breast cancer detection; however, a high level of expertise is needed for its interpretation. To overcome this difficulty, artificial intelligence (AI) algorithms for breast cancer detection have recently been investigated. In this review, we describe the characteristics of AI algorithms compared to conventional computer-aided diagnosis software and share our thoughts on the best methods to develop and validate the algorithms. Additionally, several AI algorithms have introduced for triaging screening mammograms, breast density assessment, and prediction of breast cancer risk have been introduced. Finally, we emphasize the need for interest and guidance from radiologists regarding AI research in mammography, considering the possibility that AI will be introduced shortly into clinical practice. |
format | Online Article Text |
id | pubmed-9432399 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Korean Society of Radiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-94323992022-10-12 유방촬영술에서 인공지능의 적용: 알고리즘 개발 및 평가 관점 Taehan Yongsang Uihakhoe Chi Breast Imaging Mammography is the primary imaging modality for breast cancer detection; however, a high level of expertise is needed for its interpretation. To overcome this difficulty, artificial intelligence (AI) algorithms for breast cancer detection have recently been investigated. In this review, we describe the characteristics of AI algorithms compared to conventional computer-aided diagnosis software and share our thoughts on the best methods to develop and validate the algorithms. Additionally, several AI algorithms have introduced for triaging screening mammograms, breast density assessment, and prediction of breast cancer risk have been introduced. Finally, we emphasize the need for interest and guidance from radiologists regarding AI research in mammography, considering the possibility that AI will be introduced shortly into clinical practice. The Korean Society of Radiology 2021-01 2021-01-31 /pmc/articles/PMC9432399/ /pubmed/36237466 http://dx.doi.org/10.3348/jksr.2020.0205 Text en Copyrights © 2021 The Korean Society of Radiology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Breast Imaging 유방촬영술에서 인공지능의 적용: 알고리즘 개발 및 평가 관점 |
title | 유방촬영술에서 인공지능의 적용: 알고리즘 개발 및 평가 관점 |
title_full | 유방촬영술에서 인공지능의 적용: 알고리즘 개발 및 평가 관점 |
title_fullStr | 유방촬영술에서 인공지능의 적용: 알고리즘 개발 및 평가 관점 |
title_full_unstemmed | 유방촬영술에서 인공지능의 적용: 알고리즘 개발 및 평가 관점 |
title_short | 유방촬영술에서 인공지능의 적용: 알고리즘 개발 및 평가 관점 |
title_sort | 유방촬영술에서 인공지능의 적용: 알고리즘 개발 및 평가 관점 |
topic | Breast Imaging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9432399/ https://www.ncbi.nlm.nih.gov/pubmed/36237466 http://dx.doi.org/10.3348/jksr.2020.0205 |
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