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Feasibility of using AI to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis
The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9771726/ https://www.ncbi.nlm.nih.gov/pubmed/36570770 http://dx.doi.org/10.1016/j.isci.2022.105692 |
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author | Chen, Jing Jiang, Yitao Yang, Keen Ye, Xiuqin Cui, Chen Shi, Siyuan Wu, Huaiyu Tian, Hongtian Song, Di Yao, Jincao Wang, Liping Huang, Sijing Xu, Jinfeng Xu, Dong Dong, Fajin |
author_facet | Chen, Jing Jiang, Yitao Yang, Keen Ye, Xiuqin Cui, Chen Shi, Siyuan Wu, Huaiyu Tian, Hongtian Song, Di Yao, Jincao Wang, Liping Huang, Sijing Xu, Jinfeng Xu, Dong Dong, Fajin |
author_sort | Chen, Jing |
collection | PubMed |
description | The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature entropy breast network (FEBrNet) to select responsible frames from breast ultrasound screenings and compared the diagnostic performance of AI models based on FEBrNet-recommended frames, physician-selected frames, 5-frame interval-selected frames, all frames of video, as well as that of ultrasound and mammography specialists. The AUROC of AI model based on FEBrNet-recommended frames outperformed other frame set based AI models, as well as ultrasound and mammography physicians, indicating that FEBrNet can reach level of medical specialists in frame selection.FEBrNet model can extract video responsible frames for breast nodule diagnosis, whose performance is equivalent to the doctors selected responsible frames. |
format | Online Article Text |
id | pubmed-9771726 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-97717262022-12-23 Feasibility of using AI to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis Chen, Jing Jiang, Yitao Yang, Keen Ye, Xiuqin Cui, Chen Shi, Siyuan Wu, Huaiyu Tian, Hongtian Song, Di Yao, Jincao Wang, Liping Huang, Sijing Xu, Jinfeng Xu, Dong Dong, Fajin iScience Article The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature entropy breast network (FEBrNet) to select responsible frames from breast ultrasound screenings and compared the diagnostic performance of AI models based on FEBrNet-recommended frames, physician-selected frames, 5-frame interval-selected frames, all frames of video, as well as that of ultrasound and mammography specialists. The AUROC of AI model based on FEBrNet-recommended frames outperformed other frame set based AI models, as well as ultrasound and mammography physicians, indicating that FEBrNet can reach level of medical specialists in frame selection.FEBrNet model can extract video responsible frames for breast nodule diagnosis, whose performance is equivalent to the doctors selected responsible frames. Elsevier 2022-12-05 /pmc/articles/PMC9771726/ /pubmed/36570770 http://dx.doi.org/10.1016/j.isci.2022.105692 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Chen, Jing Jiang, Yitao Yang, Keen Ye, Xiuqin Cui, Chen Shi, Siyuan Wu, Huaiyu Tian, Hongtian Song, Di Yao, Jincao Wang, Liping Huang, Sijing Xu, Jinfeng Xu, Dong Dong, Fajin Feasibility of using AI to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis |
title | Feasibility of using AI to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis |
title_full | Feasibility of using AI to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis |
title_fullStr | Feasibility of using AI to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis |
title_full_unstemmed | Feasibility of using AI to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis |
title_short | Feasibility of using AI to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis |
title_sort | feasibility of using ai to auto-catch responsible frames in ultrasound screening for breast cancer diagnosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9771726/ https://www.ncbi.nlm.nih.gov/pubmed/36570770 http://dx.doi.org/10.1016/j.isci.2022.105692 |
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