Article Data

  • Views 912
  • Dowloads 144

Short Communications

Open Access

Evaluation of AI-assisted colposcopy for detecting high-risk subtypes of human papillomavirus in CIN2

  • Takayuki Takahashi1,2
  • Hikaru Matsuoka1
  • Yusuke Kobayashi2
  • Takashi Iwata2
  • Kouji Banno2
  • Wataru Yamagami2
  • Gen Tamiya2,3,*,

1Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project, 103-0027 Tokyo, Japan

2Department of Obstetrics and Gynecology, Keio University School of Medicine, 160-8582 Tokyo, Japan

3Tohoku University Graduate School of Medicine, 980-8575 Sendai, Japan

DOI: 10.22514/ejgo.2024.020 Vol.45,Issue 1,February 2024 pp.143-146

Submitted: 04 June 2023 Accepted: 20 July 2023

Published: 15 February 2024

*Corresponding Author(s): Gen Tamiya E-mail:


Cervical cancer is the second most common cancer in women, and the role of human papillomavirus (HPV) testing in its etiology is becoming increasingly important. We aimed to evaluate the performance of an artificial intelligence (AI)-assisted colposcopy system in detecting high-risk human papillomavirus (HR-HPV) subtypes in cervical intraepithelial neoplasia (CIN) 2 patients. We conducted a post-hoc analysis of a previous observational study that developed an AI algorithm for colposcopic images of patients with CIN2. Out of 78 patients with HR-HPVs (HPV 16, 18, 31, 33, 35, 45, 52 and 58), 60 (76.9%) had positive AI colposcopy results. The accuracy, sensitivity and specificity of the AI-assisted colposcopy system in detecting HR-HPVs (16, 18, 31, 33, 35, 45, 52 and 58) were 0.689, 0.769 and 0.537, respectively. This study is the first to focus on using AI to detect high-risk subtypes. The AI algorithm accurately detects the characteristics of HR-HPVs. It can be used to detect high-risk CIN types in patients with cervical dysplasia based only on colposcopic imaging findings and is potentially a valuable tool for follow-up.


Colposcopy; Artificial intelligence; Cervical intraepithelial neoplasia; Deep learning; Human papillomavirus

Cite and Share

Takayuki Takahashi,Hikaru Matsuoka,Yusuke Kobayashi,Takashi Iwata,Kouji Banno,Wataru Yamagami,Gen Tamiya. Evaluation of AI-assisted colposcopy for detecting high-risk subtypes of human papillomavirus in CIN2. European Journal of Gynaecological Oncology. 2024. 45(1);143-146.


[1] Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 2021; 71: 209–249.

[2] Chu D, Liu T, Yao Y. Implications of viral infections and oncogenesis in uterine cervical carcinoma etiology and pathogenesis. Frontiers in Microbiology. 2023; 14: 1194431.

[3] Onuki M, Matsumoto K, Iwata T, Yamamoto K, Aoki Y, Maenohara S, et al. Human papillomavirus genotype contribution to cervical cancer and precancer: implications for screening and vaccination in Japan. Cancer Science. 2020; 111: 2546–2557.

[4] Lukic A, De Vincenzo R, Ciavattini A, Ricci C, Senatori R, Ruscito I, et al. Are we facing a new colposcopic practice in the HPV vaccination era? Opportunities, challenges, and new perspectives. Vaccines. 2021; 9: 1081.

[5] Hoste G, Vossaert K, Poppe WAJ. The clinical role of HPV testing in primary and secondary cervical cancer screening. Obstetrics and Gynecology International. 2013; 2013: 610373.

[6] Singer A, Monaghan JM, Quek SC, Deery ARS. Human papillomaviruses in pathogenesis of lower genital tract neoplasia. In Singer A, Monaghan JM, Quek SC, Deery ARS (eds.) Lower Genital Tract Precancer: Colposcopy, Pathology and Treatment (pp. 15–33). 2nd edn. Blackwell Publishing: London. 2000.

[7] Wu A, Xue P, Abulizi G, Tuerxun D, Rezhake R, Qiao Y. Artificial intelligence in colposcopic examination: a promising tool to assist junior colposcopists. Frontiers in Medicine. 2023; 10: 1060451.

[8] Fan A, Wang C, Zhang L, Yan Y, Han C, Xue F. Diagnostic value of the 2011 international federation for cervical pathology and colposcopy terminology in predicting cervical lesions. Oncotarget. 2018; 9: 9166–9176.

[9] Takahashi T, Matsuoka H, Sakurai R, Akatsuka J, Kobayashi Y, Nakamura M, et al. Development of a prognostic prediction support system for cervical intraepithelial neoplasia using artificial intelligence-based diagnosis. Journal of Gynecologic Oncology. 2022; 33: e57.

[10] Massad LS, Einstein MH, Huh WK, Katki HA, Kinney WK, Schiffman M, et al. 2012 updated consensus guidelines for the management of abnormal cervical cancer screening tests and cancer precursors. Obstetrics & Gynecology. 2013; 121: 829–846.

[11] Kondo K, Uenoyama A, Kitagawa R, Tsunoda H, Kusumoto-Matsuo R, Mori S, et al. Genotype distribution of human papillomaviruses in Japanese women with abnormal cervical cytology. The Open Virology Journal. 2012; 6: 277–283.

[12] Matsumoto K, Oki A, Furuta R, Maeda H, Yasugi T, Takatsuka N, et al. Predicting the progression of cervical precursor lesions by human papillomavirus genotyping: a prospective cohort study. International Journal of Cancer. 2011; 128: 2898–2910.

[13] Gravitt PE, Peyton CL, Alessi TQ, Wheeler CM, Coutlée F, Hildesheim A, et al. Improved amplification of genital human papillomaviruses. Journal of Clinical Microbiology. 2000; 38: 357–361.

[14] Pimple SA, Mishra GA. Global strategies for cervical cancer prevention and screening. Minerva Ginecologica. 2019; 71: 313–320.

[15] Bhatla N, Singhal S. Primary HPV screening for cervical cancer. Best Practice & Research Clinical Obstetrics & Gynaecology. 2020; 65: 98–108.

[16] Bedell SL, Goldstein LS, Goldstein AR, Goldstein AT. Cervical cancer screening: past, present, and future. Sexual Medicine Reviews. 2020; 8: 28–37.

[17] Zhou Q, Gong Y, Qiu X, Sui L, Zhang H, Wang Y, et al. Visual appearance of the uterine cervix differs on the basis of HPV type status in high-grade squamous intraepithelial lesion: the results of a reliable method. BMC Women’s Health. 2022; 22: 24.

[18] Rema PN, Mathew A, Thomas S. Performance of colposcopic scoring by modified international federation of cervical pathology and colposcopy terminology for diagnosing cervical intraepithelial neoplasia in a low-resource setting. South Asian Journal of Cancer. 2019; 08: 218–220.

[19] Scheungraber C, Koenig U, Fechtel B, Kuehne-Heid R, Duerst M, Schneider A. The colposcopic feature ridge sign is associated with the presence of cervical intraepithelial neoplasia 2/3 and human papillomavirus 16 in young women. Journal of Lower Genital Tract Disease. 2009; 13: 13–16.

[20] Zaal A, Louwers JA, Berkhof J, Kocken M, Ter Harmsel WA, Graziosi GC, et al. Agreement between colposcopic impression and histological diagnosis among human papillomavirus type 16-positive women: a clinical trial using dynamic spectral imaging colposcopy. BJOG: an International Journal of Obstetrics & Gynaecology. 2012; 119: 537–544.

[21] Sone K, Toyohara Y, Taguchi A, Miyamoto Y, Tanikawa M, Uchino-Mori M, et al. Application of artificial intelligence in gynecologic malignancies: a review. Journal of Obstetrics and Gynaecology Research. 2021; 47: 2577–2585.

Abstracted / indexed in

Science Citation Index Expanded (SciSearch) Created as SCI in 1964, Science Citation Index Expanded now indexes over 9,500 of the world’s most impactful journals across 178 scientific disciplines. More than 53 million records and 1.18 billion cited references date back from 1900 to present.

Biological Abstracts Easily discover critical journal coverage of the life sciences with Biological Abstracts, produced by the Web of Science Group, with topics ranging from botany to microbiology to pharmacology. Including BIOSIS indexing and MeSH terms, specialized indexing in Biological Abstracts helps you to discover more accurate, context-sensitive results.

Google Scholar Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines.

JournalSeek Genamics JournalSeek is the largest completely categorized database of freely available journal information available on the internet. The database presently contains 39226 titles. Journal information includes the description (aims and scope), journal abbreviation, journal homepage link, subject category and ISSN.

Current Contents - Clinical Medicine Current Contents - Clinical Medicine provides easy access to complete tables of contents, abstracts, bibliographic information and all other significant items in recently published issues from over 1,000 leading journals in clinical medicine.

BIOSIS Previews BIOSIS Previews is an English-language, bibliographic database service, with abstracts and citation indexing. It is part of Clarivate Analytics Web of Science suite. BIOSIS Previews indexes data from 1926 to the present.

Journal Citation Reports/Science Edition Journal Citation Reports/Science Edition aims to evaluate a journal’s value from multiple perspectives including the journal impact factor, descriptive data about a journal’s open access content as well as contributing authors, and provide readers a transparent and publisher-neutral data & statistics information about the journal.

Submission Turnaround Time