Nature Communications (Jun 2021)
Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears
- 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
Affiliations
- Xiaohui Zhu
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University
- Xiaoming Li
- Department of Pathology, Shenzhen Bao’an People’s Hospital (group)
- Kokhaur Ong
- Institute of Molecular and Cell Biology, A*STAR
- Wenli Zhang
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University
- Wencai Li
- The First Affiliated Hospital of Zhengzhou University
- Longjie Li
- Institute of Molecular and Cell Biology, A*STAR
- David Young
- Institute of Molecular and Cell Biology, A*STAR
- Yongjian Su
- Guangzhou F.Q.PATHOTECH Co., Ltd
- Bin Shang
- Guangzhou F.Q.PATHOTECH Co., Ltd
- Linggan Peng
- Guangzhou F.Q.PATHOTECH Co., Ltd
- Wei Xiong
- Guangzhou Kaipu Biotechnology Co., Ltd
- Yunke Liu
- Laboratory Department, Guangzhou Tianhe District Maternal and Child Health Care Hospital
- Wenting Liao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center
- Jingjing Xu
- The First Affiliated Hospital of Zhengzhou University
- Feifei Wang
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University
- Qing Liao
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University
- Shengnan Li
- Guangzhou F.Q.PATHOTECH Co., Ltd
- Minmin Liao
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University
- Yu Li
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University
- Linshang Rao
- Guangzhou F.Q.PATHOTECH Co., Ltd
- Jinquan Lin
- Guangzhou F.Q.PATHOTECH Co., Ltd
- Jianyuan Shi
- Guangzhou F.Q.PATHOTECH Co., Ltd
- Zejun You
- Guangzhou F.Q.PATHOTECH Co., Ltd
- Wenlong Zhong
- Guangzhou Huayin medical inspection center Co., Ltd
- Xinrong Liang
- Guangzhou Huayin medical inspection center Co., Ltd
- Hao Han
- Institute of Molecular and Cell Biology, A*STAR
- Yan Zhang
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University
- Na Tang
- Department of Pathology, Shenzhen First People’s Hospital
- Aixia Hu
- Department of Pathology, Henan Provincial People’s Hospital
- Hongyi Gao
- Department of Pathology, Guangdong Provincial Women’s and Children’s Dispensary
- Zhiqiang Cheng
- Department of Pathology, Shenzhen First People’s Hospital
- Li Liang
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University
- Weimiao Yu
- Institute of Molecular and Cell Biology, A*STAR
- Yanqing Ding
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University
- DOI
- https://doi.org/10.1038/s41467-021-23913-3
- Journal volume & issue
-
Vol. 12,
no. 1
pp. 1 – 12
Abstract
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.
