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

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

Technical advancements have significantly improved early diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various practical factors. Here, the authors develop an artificial intelligence assistive diagnostic solution to improve cervical liquid-based thin-layer cell smear...

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Autores principales: Xiaohui Zhu, Xiaoming Li, Kokhaur Ong, Wenli Zhang, Wencai Li, Longjie Li, David Young, Yongjian Su, Bin Shang, Linggan Peng, Wei Xiong, Yunke Liu, Wenting Liao, Jingjing Xu, Feifei Wang, Qing Liao, Shengnan Li, Minmin Liao, Yu Li, Linshang Rao, Jinquan Lin, Jianyuan Shi, Zejun You, Wenlong Zhong, Xinrong Liang, Hao Han, Yan Zhang, Na Tang, Aixia Hu, Hongyi Gao, Zhiqiang Cheng, Li Liang, Weimiao Yu, Yanqing Ding
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/a9ff146cba1f4537910e4022e670fbb3
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Sumario:Technical advancements have significantly improved early diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various practical factors. Here, the authors develop an artificial intelligence assistive diagnostic solution to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria in a large multicenter study.

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