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Chinese Journal of Clinical Laboratory Management(Electronic Edition) ›› 2019, Vol. 07 ›› Issue (04): 193-198. doi: 10.3877/cma.j.issn.2095-5820.2019.04.001

Special Issue:

• Feperts Forum •     Next Articles

Applicational progress and challenges of the artificial intelligence-aided cervical cancer cytological screening

Shuanlong Che1, Dong Liu1, Si Liu2, Pifu Luo1,()   

  1. 1. Pathology Center, Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou 51005, China
    2. Big Data Center, Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou 51005, China
  • Received:2019-08-14 Online:2019-11-28 Published:2019-11-28
  • Contact: Pifu Luo
  • About author:
    Corresponding author: Luo Pifu, Email:

Abstract:

Cervical cancer is one of the most common malignant tumors in women. Early detection and treatment are critical to reduce its mobility and motality. Cytological screening combined with HPV test is the best way for its early detection. However, the early diagnosis is impeded due to severely lack of cytopathologists. The application of artificial intelligency (AI) technology in cervical cancer screening will provide the best solution to enhance the screening efficiency and quality. We reviewed literatures of the AI-aided cervical cancer screening, described its progress of AI algorithm models, human screening and AI-aided screening interactive models in the cervical cytology; described the Prons and Cons of different machine and deep learning algorithms based on the bright and dark rules; analyzed available results of the AI-aided cervical cancer screening, and diacussed problems and challenges in exploring and applying of the AI-aided cervical cancer screening products. The purpose of this review is to provide insights for the research and development of the AI-aided cervical cancer screening to promote its application and implementation, which will contribute to reduce the mobility and motality of cervical cancer.

Key words: Cervical cancer, Cytological screening, Artificial intelligence, Deep learning, Machine learning

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