Tong Wu, Eric Lucas, Fanghui Zhao, Partha Basu, Youlin Qiao
{"title":"Artificial intelligence strengthenes cervical cancer screening - present and future.","authors":"Tong Wu, Eric Lucas, Fanghui Zhao, Partha Basu, Youlin Qiao","doi":"10.20892/j.issn.2095-3941.2024.0198","DOIUrl":null,"url":null,"abstract":"<p><p>Cervical cancer is a severe threat to women's health. The majority of cervical cancer cases occur in developing countries. The WHO has proposed screening 70% of women with high-performance tests between 35 and 45 years of age by 2030 to accelerate the elimination of cervical cancer. Due to an inadequate health infrastructure and organized screening strategy, most low- and middle-income countries are still far from achieving this goal. As part of the efforts to increase performance of cervical cancer screening, it is necessary to investigate the most accurate, efficient, and effective methods and strategies. Artificial intelligence (AI) is rapidly expanding its application in cancer screening and diagnosis and deep learning algorithms have offered human-like interpretation capabilities on various medical images. AI will soon have a more significant role in improving the implementation of cervical cancer screening, management, and follow-up. This review aims to report the state of AI with respect to cervical cancer screening. We discuss the primary AI applications and development of AI technology for image recognition applied to detection of abnormal cytology and cervical neoplastic diseases, as well as the challenges that we anticipate in the future.</p>","PeriodicalId":9611,"journal":{"name":"Cancer Biology & Medicine","volume":" ","pages":""},"PeriodicalIF":5.6000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523278/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Biology & Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.20892/j.issn.2095-3941.2024.0198","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Cervical cancer is a severe threat to women's health. The majority of cervical cancer cases occur in developing countries. The WHO has proposed screening 70% of women with high-performance tests between 35 and 45 years of age by 2030 to accelerate the elimination of cervical cancer. Due to an inadequate health infrastructure and organized screening strategy, most low- and middle-income countries are still far from achieving this goal. As part of the efforts to increase performance of cervical cancer screening, it is necessary to investigate the most accurate, efficient, and effective methods and strategies. Artificial intelligence (AI) is rapidly expanding its application in cancer screening and diagnosis and deep learning algorithms have offered human-like interpretation capabilities on various medical images. AI will soon have a more significant role in improving the implementation of cervical cancer screening, management, and follow-up. This review aims to report the state of AI with respect to cervical cancer screening. We discuss the primary AI applications and development of AI technology for image recognition applied to detection of abnormal cytology and cervical neoplastic diseases, as well as the challenges that we anticipate in the future.
期刊介绍:
Cancer Biology & Medicine (ISSN 2095-3941) is a peer-reviewed open-access journal of Chinese Anti-cancer Association (CACA), which is the leading professional society of oncology in China. The journal quarterly provides innovative and significant information on biological basis of cancer, cancer microenvironment, translational cancer research, and all aspects of clinical cancer research. The journal also publishes significant perspectives on indigenous cancer types in China.