| Literature DB >> 34112790 |
Xiaohui Zhu1,2, Xiaoming Li3, Kokhaur Ong4,5, Wenli Zhang1,2, Wencai Li6, Longjie Li4, David Young4, Yongjian Su7, Bin Shang7, Linggan Peng7, Wei Xiong8, Yunke Liu9, Wenting Liao10, Jingjing Xu6, Feifei Wang1,2, Qing Liao1,2, Shengnan Li7, Minmin Liao1,2, Yu Li1,2, Linshang Rao7, Jinquan Lin7, Jianyuan Shi7, Zejun You7, Wenlong Zhong11, Xinrong Liang11, Hao Han4, Yan Zhang1,12, Na Tang13, Aixia Hu14, Hongyi Gao15, Zhiqiang Cheng16, Li Liang17,18, Weimiao Yu19,20, Yanqing Ding21,22.
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 <180s/slide. Our system is adaptive to sample preparation using different standards, staining protocols and scanners.Entities:
Year: 2021 PMID： 34112790 DOI： 10.1038/s41467-021-23913-3
Source DB: PubMed Journal: Nat Commun ISSN： 2041-1723 Impact factor: 14.919