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 () and Yanqing Ding ()
Additional contact information
Xiaohui Zhu: Southern Medical University
Xiaoming Li: Shenzhen Bao’an People’s Hospital (group)
Kokhaur Ong: A*STAR
Wenli Zhang: Southern Medical University
Wencai Li: The First Affiliated Hospital of Zhengzhou University
Longjie Li: A*STAR
David Young: 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: Guangzhou Tianhe District Maternal and Child Health Care Hospital
Wenting Liao: Sun Yat-sen University Cancer Center
Jingjing Xu: The First Affiliated Hospital of Zhengzhou University
Feifei Wang: Southern Medical University
Qing Liao: Southern Medical University
Shengnan Li: Guangzhou F.Q.PATHOTECH Co., Ltd
Minmin Liao: Southern Medical University
Yu Li: 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: A*STAR
Yan Zhang: Southern Medical University
Na Tang: Shenzhen First People’s Hospital
Aixia Hu: Henan Provincial People’s Hospital
Hongyi Gao: Guangdong Provincial Women’s and Children’s Dispensary
Zhiqiang Cheng: Shenzhen First People’s Hospital
Li Liang: Southern Medical University
Weimiao Yu: A*STAR
Yanqing Ding: Southern Medical University

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract 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

Date: 2021
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DOI: 10.1038/s41467-021-23913-3

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