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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: gen.tamiya@riken.jp

Abstract

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.


Keywords

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.

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