Nature communications · 2021
Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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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
Аннотация
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.