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Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears

Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according...

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Autores principales: Zhu, Xiaohui, Li, Xiaoming, Ong, Kokhaur, Zhang, Wenli, Li, Wencai, Li, Longjie, Young, David, Su, Yongjian, Shang, Bin, Peng, Linggan, Xiong, Wei, Liu, Yunke, Liao, Wenting, Xu, Jingjing, Wang, Feifei, Liao, Qing, Li, Shengnan, Liao, Minmin, Li, Yu, Rao, Linshang, Lin, Jinquan, Shi, Jianyuan, You, Zejun, Zhong, Wenlong, Liang, Xinrong, Han, Hao, Zhang, Yan, Tang, Na, Hu, Aixia, Gao, Hongyi, Cheng, Zhiqiang, Liang, Li, Yu, Weimiao, Ding, Yanqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192526/
https://www.ncbi.nlm.nih.gov/pubmed/34112790
http://dx.doi.org/10.1038/s41467-021-23913-3
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author Zhu, Xiaohui
Li, Xiaoming
Ong, Kokhaur
Zhang, Wenli
Li, Wencai
Li, Longjie
Young, David
Su, Yongjian
Shang, Bin
Peng, Linggan
Xiong, Wei
Liu, Yunke
Liao, Wenting
Xu, Jingjing
Wang, Feifei
Liao, Qing
Li, Shengnan
Liao, Minmin
Li, Yu
Rao, Linshang
Lin, Jinquan
Shi, Jianyuan
You, Zejun
Zhong, Wenlong
Liang, Xinrong
Han, Hao
Zhang, Yan
Tang, Na
Hu, Aixia
Gao, Hongyi
Cheng, Zhiqiang
Liang, Li
Yu, Weimiao
Ding, Yanqing
author_facet Zhu, Xiaohui
Li, Xiaoming
Ong, Kokhaur
Zhang, Wenli
Li, Wencai
Li, Longjie
Young, David
Su, Yongjian
Shang, Bin
Peng, Linggan
Xiong, Wei
Liu, Yunke
Liao, Wenting
Xu, Jingjing
Wang, Feifei
Liao, Qing
Li, Shengnan
Liao, Minmin
Li, Yu
Rao, Linshang
Lin, Jinquan
Shi, Jianyuan
You, Zejun
Zhong, Wenlong
Liang, Xinrong
Han, Hao
Zhang, Yan
Tang, Na
Hu, Aixia
Gao, Hongyi
Cheng, Zhiqiang
Liang, Li
Yu, Weimiao
Ding, Yanqing
author_sort Zhu, Xiaohui
collection PubMed
description Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria. We train AIATBS with >81,000 retrospective samples. It integrates YOLOv3 for target detection, Xception and Patch-based models to boost target classification, and U-net for nucleus segmentation. We integrate XGBoost and a logical decision tree with these models to optimize the parameters given by the learning process, and we develop a complete cervical liquid-based cytology smear TBS diagnostic system which also includes a quality control solution. We validate the optimized system with >34,000 multicenter prospective samples and achieve better sensitivity compared to senior cytologists, yet retain high specificity while achieving a speed of <180s/slide. Our system is adaptive to sample preparation using different standards, staining protocols and scanners.
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spelling pubmed-81925262021-07-01 Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears Zhu, Xiaohui Li, Xiaoming Ong, Kokhaur Zhang, Wenli Li, Wencai Li, Longjie Young, David Su, Yongjian Shang, Bin Peng, Linggan Xiong, Wei Liu, Yunke Liao, Wenting Xu, Jingjing Wang, Feifei Liao, Qing Li, Shengnan Liao, Minmin Li, Yu Rao, Linshang Lin, Jinquan Shi, Jianyuan You, Zejun Zhong, Wenlong Liang, Xinrong Han, Hao Zhang, Yan Tang, Na Hu, Aixia Gao, Hongyi Cheng, Zhiqiang Liang, Li Yu, Weimiao Ding, Yanqing Nat Commun Article Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria. We train AIATBS with >81,000 retrospective samples. It integrates YOLOv3 for target detection, Xception and Patch-based models to boost target classification, and U-net for nucleus segmentation. We integrate XGBoost and a logical decision tree with these models to optimize the parameters given by the learning process, and we develop a complete cervical liquid-based cytology smear TBS diagnostic system which also includes a quality control solution. We validate the optimized system with >34,000 multicenter prospective samples and achieve better sensitivity compared to senior cytologists, yet retain high specificity while achieving a speed of <180s/slide. Our system is adaptive to sample preparation using different standards, staining protocols and scanners. Nature Publishing Group UK 2021-06-10 /pmc/articles/PMC8192526/ /pubmed/34112790 http://dx.doi.org/10.1038/s41467-021-23913-3 Text en © The Author(s) 2021 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zhu, Xiaohui
Li, Xiaoming
Ong, Kokhaur
Zhang, Wenli
Li, Wencai
Li, Longjie
Young, David
Su, Yongjian
Shang, Bin
Peng, Linggan
Xiong, Wei
Liu, Yunke
Liao, Wenting
Xu, Jingjing
Wang, Feifei
Liao, Qing
Li, Shengnan
Liao, Minmin
Li, Yu
Rao, Linshang
Lin, Jinquan
Shi, Jianyuan
You, Zejun
Zhong, Wenlong
Liang, Xinrong
Han, Hao
Zhang, Yan
Tang, Na
Hu, Aixia
Gao, Hongyi
Cheng, Zhiqiang
Liang, Li
Yu, Weimiao
Ding, Yanqing
Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears
title Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears
title_full Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears
title_fullStr Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears
title_full_unstemmed Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears
title_short Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears
title_sort hybrid ai-assistive diagnostic model permits rapid tbs classification of cervical liquid-based thin-layer cell smears
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192526/
https://www.ncbi.nlm.nih.gov/pubmed/34112790
http://dx.doi.org/10.1038/s41467-021-23913-3
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