Science and Innovations in MedicineScience and Innovations in MedicineНаука и инновации в медицине2500-13882618-754XFSBEI of Higher Education SamSMU of Ministry of Health of the Russian Federation64082810.35693/SIM640828Obstetrics and GynecologyАкушерство и гинекологияReview ArticleCervical screening and artificial intelligenceЦервикальный скрининг и искусственный интеллектhttps://orcid.org/0000-0002-9483-8909KolsanovaAnna V.КолсановаА. В.
Russian Federation

PhD, Associate professor, Head of the Department of Obstetrics and Gynecology of the Institute of Pediatrics

д-р мед. наук, доцент, заведующая кафедрой акушерства и гинекологии института педиатрии

a.v.kazakova@samsmu.ruhttps://orcid.org/0000-0002-3890-9944ChechkoSvetlana M.ЧечкоС. М.
Russian Federation

assistant of the Department of Obstetrics and Gynecology at the Institute of Pediatrics

ассистент кафедры акушерства и гинекологии института педиатрии

svetlana-chechko92@mail.ruhttps://orcid.org/0000-0002-1376-7361KiraEvgeny F.КираЕ. Ф.
Russian Federation

PhD, Professor, Academician of the Russian Academy of Natural Sciences, Advisor to the Medical Director

д-р мед. наук, профессор, академик РАЕН, советник медицинского директора

profkira33@gmail.comhttps://orcid.org/0009-0009-5765-2361ShamshatdinovaAliya R.ШамшатдиноваА. Р.
Russian Federation

1-year resident of the Department of Obstetrics and Gynecology of the IPЕ, senior laboratory assistant of the Department of Obstetrics and Gynecology of the Institute of Pediatrics

ординатор 1 года обучения кафедры акушерства и гинекологии ИПО, старший лаборант кафедры акушерства и гинекологии института педиатрии

Aliyashamshat@gmail.comSamara State Medical UniversityФГБОУ ВО «Самарский государственный медицинский университет» Минздрава РоссииMEDSI Group of Companies, Medical AcademyАО ГК «МЕДСИ», Медицинская академия2811202415122024942462500311202423112024Copyright ©; 2024, Kolsanova A.V., Chechko S.M., Kira E.F., Shamshatdinova A.R.Copyright ©; 2024, Колсанова А.В., Чечко С.М., Кира Е.Ф., Шамшатдинова А.Р.2024Kolsanova A.V., Chechko S.M., Kira E.F., Shamshatdinova A.R.Колсанова А.В., Чечко С.М., Кира Е.Ф., Шамшатдинова А.Р.https://creativecommons.org/licenses/by/4.0https://innoscience.ru/2500-1388/article/view/640828

Currently, the use of artificial intelligence (AI) in gynecology is at the initial stage of its implementation. To date, cervical cancer (cervical cancer) is the second most common malignant tumor. Untimely diagnosis of the disease has a serious impact primarily in remote regions of the country, which is directly related to the lack of laboratory equipment, difficulties in transporting materials, as well as the lack of highly qualified cytologists and colposcopists. AI-based programs for reading cytological images, HPV identification and colposcopy have been created to date, which makes it possible to increase the availability of visual screening for women throughout the country, including those living in remote regions. In addition, it helps to improve the timely diagnosis of breast cancer in women through cervical screening using AI systems. The review presents the main categories of AI, including machine learning methods, and includes foreign and domestic research on AI-based technologies for performing cytological examination and colposcopy, published between 2019 and 2024. The search for literature sources was conducted on the PubMed platform. The search queries included the following keywords: “cervical screening”, “artificial intelligence in gynecology”, “artificial intelligence in colposcopy”, “artificial intelligence in cervical screening". It was found that AI programs for the interpretation of Pap smear (Al-Pap) are 5.8% more sensitive to the detection of CIN2+ than manual counting with a slight decrease in specificity. In studies based on AI processing of colposcopic images, the percentage of coincidence between the results and the histological conclusion was higher than when interpreted by specialist doctors by 16.64%. When identifying HSIL+ with artificial intelligence, a higher sensitivity was revealed, 11.5% higher than the conclusion of the colposcopist, while the specificity was practically comparable. The Russian Federation is actively developing a domestic digital portable colposcope on the basis of the Samara State Medical University of the Ministry of Health of the Russian Federation, together with specialists from the Almazov National Medical Research Center of the Ministry of Health of the Russian Federation, as well as the Peter the Great St. Petersburg Polytechnic University for reading and interpreting colposcopic images.

Применение искусственного интеллекта в гинекологии находится в настоящее время на начальном этапе. На основе искусственного интеллекта уже созданы программы для чтения цитологических изображений, идентификации вируса папилломы человека и кольпоскопии, что повышает доступность визуального скрининга для женщин, в том числе проживающих в отдаленных районах. Применение систем искусственного интеллекта для цервикального скрининга способствует улучшению своевременной диагностики рака шейки матки.

В обзоре представлены результаты зарубежных и отечественных научных работ, посвященных технологиям использования искусственного интеллекта для выполнения цитологического исследования и кольпоскопии. Поиск источников литературы, опубликованных в период с 2019 по 2024 год, проводился на платформе PubMed. Поисковые запросы включали следующие ключевые слова: “cervical screening”, “artificial intelligence in gynecology”, “artificial intelligence in colposcopy”, “artificial intelligence in cervical screening”.

Установлено, что программы искусственного интеллекта для интерпретации мазка по Папаниколау (Al-Pap) на 5,8% более чувствительны к обнаружению CIN2+, чем ручной подсчет, с небольшим снижением специфичности.

В исследованиях на основе обработки искусственным интеллектом кольпоскопических картин процент совпадения результатов и гистологическим заключением был выше, чем при интерпретации врачами-специалистами, на 16,64 %. При идентификации HSIL+ искусственным интеллектом выявлена более высокая чувствительность, на 11,5% превышающая заключение кольпоскописта, в то время как специфичность была практически сопоставима. Кроме того, программы на основе ИИ оказались более точными в прогнозировании мест проведения биопсии.

artificial intelligencedigital colposcopycervical screeningискусственный интеллектцервикальный скринингцифровая кольпоскопияDhombres F, Bonnard J, Bailly K, et al. Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review. J Med Internet Res. 2022;24(4):e35465. DOI: https://doi.org/10.2196/35465Yin J, Ngiam KY, Teo HH. Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. J Med Internet Res. 2021;23(4):e25759. DOI: https://doi.org/10.2196/25759Xu J, Xue K, Zhang K. Current status and future trends of clinical diagnoses via image-based deep learning. Theranostics. 2019;9(25):7556-7565. DOI: https://doi.org/10.7150/thno.38065Francesconi E. The winter, the summer and the summer dream of artificial intelligence in law: Presidential address to the 18th International Conference on Artificial Intelligence and Law. Artif Intell Law (Dordr). 2022;30(2):147-161. DOI: https://doi.org/10.1007/s10506-022-09309-8Ashrafian H, Darzi A, Athanasiou T. A novel modification of the Turing test for artificial intelligence and robotics in healthcare. Int J Med Robot. 2015;11(1):38-43. DOI: https://doi.org/10.1002/rcs.1570Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:36-40. DOI: https://doi.org/10.1016/j.metabol.2017.01.011Muthukrishnan N, Maleki F, Ovens K, et al. Brief History of Artificial Intelligence. Neuroimaging Clin N Am. 2020;30(4):393-399. DOI: https://doi.org/10.1016/j.nic.2020.07.004Howard J. Artificial intelligence: Implications for the future of work. Am J Ind Med. 2019;62(11):917-926. DOI: https://doi.org/10.1002/ajim.23037Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230-243. DOI: https://doi.org/10.1136/svn-2017-000101Rashidi HH, Tran N, Albahra S, et al. Machine learning in health care and laboratory medicine: General overview of supervised learning and Auto-ML. Int J Lab Hematol. 2021;43(1):15-22. DOI: https://doi.org/10.1111/ijlh.13537Cleret de Langavant L, Bayen E, Yaffe K. Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study. J Med Internet Res. 2018;20(7):e10493. DOI: https://doi.org/10.2196/10493Fedotov VA. Artificial intelligence: advantages and disadvantages. Scientific electronic journal Meridian. 2021;2(55):27-29. (In Russ.). [Федотов В.А. Искусственный интеллект: преимущества и недостатки. Научный электронный журнал Меридиан. 2021;2(55):27-29]. URL: https://elibrary.ru/download/elibrary_44745539_73779427.pdfNesterova EA. On the issue of artificial intelligence in the context of human development. In: Man and society: history and modernity. 2024;138-142. (In Russ.). [Нестерова Е.А. К вопросу об искусственном интеллекте в контексте развития. В сб.: Человек и общество: история и современность. 2024;138-142].Kaprin AD, Starinskiy VV, Shakhzadova AO. State of oncological care for the population of Russia in 2021. M., 2022. (In Russ.). [Каприн А.Д., Старинский В.В., Шахзадова А.О. Состояние онкологической помощи населению России в 2021 году. М., 2022]. ISBN 978-5-85502-297-1Cohen PA, Jhingran A, Oaknin A, et al. Cervical cancer. Lancet. 2019;393:169-182. DOI: https://doi.org/10.1016/s0140-6736(18)32470-xWatson M, et al. Surveillance of high-grade cervical cancer precursors (CIN III/AIS) in four population-based cancer registries, United States, 2009–2012. Prev Med. 2017;103:60-65. DOI: https://doi.org/10.1016/j.ypmed.2017.07.027Ahmed SR, Befano B, Lemay A, et al. Reproducible and clinically translatable deep neural networks for cancer screening. Preprint. Sci Rep. 2023;rs.3.rs-2526701. DOI: https://doi.org/10.21203/rs.3.rs-2526701/v1Wang CW, Liou YA, Lin YJ, et al. Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning. Sci Rep. 2021;11(1):16244. DOI: https://doi.org/10.1038/s41598-021-95545-yBao H, Bi H, Zhang X, et al. Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: A multicenter, clinical-based, observational study. Gynecol Oncol. 2020;159(1):171-178. DOI: https://doi.org/10.1016/j.ygyno.2020.07.099Song T, et al. Screening capacity and cost-effectiveness of the human papillomavirus test versus cervicography as an adjunctive test to Pap cytology to detect high-grade cervical dysplasia. Eur J Obstet Gynecol Reprod Biol. 2019;234:112-116. DOI: https://doi.org/10.1016/j.ejogrb.2019.01.008Bao H, Sun X, Zhang Y, et al. The artificial intelligence-assisted cytology diagnostic system in large-scale cervical cancer screening: A population-based cohort study of 0.7 million women. Cancer Med. 2020;9(18):6896-6906. DOI: https://doi.org/10.1002/cam4.3296Xue P, Xu HM, Tang HP, et al. Assessing artificial intelligence enabled liquid-based cytology for triaging HPV-positive women: a population-based cross-sectional study. Acta Obstet Gynecol Scand. 2023;102(8):1026-1033. DOI: https://doi.org/10.1111/aogs.14611Shen M, Zou Z, Bao H, et al. Cost-effectiveness of artificial intelligence-assisted liquid-based cytology testing for cervical cancer screening in China. Lancet Reg Health West Pac. 2023;34:100726. DOI: https://doi.org/10.1016/j.lanwpc.2023.100726Dercle L, Lu L, Schwartz LH, et al. Radiomics response signature for identification of metastatic colorectal cancer sensitive to therapies targeting EGFR pathway. J Natl Cancer Inst. 2020;112(9):902-12. DOI: https://doi.org/10.1093/jnci/djaa017Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J Natl Cancer Inst. 2019;111(9):916-22. DOI: https://doi.org/10.1093/jnci/djy222Brandão M, Mendes F, Martins M, et al. Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. J Clin Med. 2024;13(4):1061. DOI: https://doi.org/10.3390/jcm13041061Stuebs FA, Schulmeyer CE, Mehlhorn G, et al. Accuracy of colposcopy-directed biopsy in detecting early cervical neoplasia: a retrospective study. Arch Gynecol Obstet. 2019;299(2):525-532. DOI: https://doi.org/10.1007/s00404-018-4953-8Hou X, Shen G, Zhou L, et al. Artificial Intelligence in Cervical Cancer Screening and Diagnosis. Front Oncol. 2022;12:851367. DOI: https://doi.org/10.3389/fonc.2022.851367Bray F, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(4):394-424. DOI: https://doi.org/10.3322/caac.21492Chandran V, et al. Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images. Biomed Res Int. 2021;2021:5584004. DOI: https://doi.org/10.1155/2021/5584004Champin D, Ramirez-Soto MC, Vargas-Herrera J. Use of Smartphones for the detection of uterine cervical cancer: A systematic review. Cancers. 2021;13(23):6047. DOI: https://doi.org/10.3390/cancers13236047Alrajjal A, Pansare V, Choudhury MSR, et al. Squamous intraepithelial lesions (SIL: LSIL, HSIL, ASCUS, ASC-H, LSIL-H) of Uterine Cervix and Bethesda System. Cytojournal. 2021;18:16. DOI: https://doi.org/10.25259/Cytojournal_24_2021Rebolj M, et al. A daunting challenge: Human Papillomavirus assays and cytology in primary cervical screening of women below age 30 years. Eur J Cancer. 2015;51(11):1456-1466. DOI: https://doi.org/10.1016/j.ejca.2015.04.012Hu L, Bell D, Antani S, et al. An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening. J Natl Cancer Inst. 2019;111(9):923-932. DOI: https://doi.org/10.1093/jnci/djy225Xue P, Tang C, Li Q, et al. Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies. BMC Med. 2020;18(1):406. DOI: https://doi.org/10.1186/s12916-020-01860-yWu A, Xue P, Abulizi G, et al. Artificial intelligence in colposcopic examination: A promising tool to assist junior colposcopists. Front Med (Lausanne). 2023;10:1060451. DOI: https://doi.org/10.3389/fmed.2023.1060451Ouh YT, Kim TJ, Ju W, et al. Development and validation of artificial intelligence-based analysis software to support screening system of cervical intraepithelial neoplasia. Sci Rep. 2024;14(1):1957. DOI: https://doi.org/10.1038/s41598-024-51880-4Kim S, Lee H, Lee S, et al. Role of Artificial Intelligence Interpretation of Colposcopic Images in Cervical Cancer Screening. Healthcare (Basel). 2022;10(3):468. DOI: https://doi.org/10.3390/healthcare10030468Khan MJ, et al. ASCCP colposcopy standards: Role of colposcopy, benefits, potential harms, and terminology for colposcopic practice. J Low Genit Tract Dis. 2017;21(4), 223-229. DOI: https://doi.org/10.1097/LGT.0000000000000338Akazawa M, Hashimoto K. Artificial intelligence in gynecologic cancers: Current status and future challenges–A systematic review. Artif Intell Med. 2021;120:102164. DOI: https://doi.org/10.1016/j.artmed.2021.102164