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

Author

Listed:
  • Xiaohui Zhu

    (Southern Medical University
    Guangdong Province Key Laboratory of Molecular Tumor Pathology)

  • Xiaoming Li

    (Shenzhen Bao’an People’s Hospital (group))

  • Kokhaur Ong

    (A*STAR
    A*STAR)

  • Wenli Zhang

    (Southern Medical University
    Guangdong Province Key Laboratory of Molecular Tumor Pathology)

  • 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
    Guangdong Province Key Laboratory of Molecular Tumor Pathology)

  • Qing Liao

    (Southern Medical University
    Guangdong Province Key Laboratory of Molecular Tumor Pathology)

  • Shengnan Li

    (Guangzhou F.Q.PATHOTECH Co., Ltd)

  • Minmin Liao

    (Southern Medical University
    Guangdong Province Key Laboratory of Molecular Tumor Pathology)

  • Yu Li

    (Southern Medical University
    Guangdong Province Key Laboratory of Molecular Tumor Pathology)

  • 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
    Shenzhen Longhua District Maternity & Child Healthcare Hospital)

  • 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
    Guangdong Province Key Laboratory of Molecular Tumor Pathology)

  • Weimiao Yu

    (A*STAR
    A*STAR)

  • Yanqing Ding

    (Southern Medical University
    Guangdong Province Key Laboratory of Molecular Tumor Pathology)

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

Suggested Citation

  • 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, 2021. "Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23913-3
    DOI: 10.1038/s41467-021-23913-3
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    Cited by:

    1. Jiaxin Bai & Ning Li & Hua Ye & Xu Li & Li Chen & Junbo Hu & Baochuan Pang & Xiaodong Chen & Gong Rao & Qinglei Hu & Shijie Liu & Si Sun & Cheng Li & Xiaohua Lv & Shaoqun Zeng & Jing Cai & Shenghua Ch, 2025. "AI-assisted cervical cytology precancerous screening for high-risk population in resource-limited regions using a compact microscope," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    2. Peng Xue & Le Dang & Ling-Hua Kong & Hong-Ping Tang & Hai-Miao Xu & Hai-Yan Weng & Zhe Wang & Rong-Gan Wei & Lian Xu & Hong-Xia Li & Hai-Yan Niu & Ming-Juan Wang & Zi-Chen Ye & Zhi-Fang Li & Wen Chen , 2025. "Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection," Nature Communications, Nature, vol. 16(1), pages 1-10, December.

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