Artificial intelligence and visual inspection in cervical cancer screening

Carolyn Nakisige, Marlieke De Fouw, Johnblack Kabukye, Marat Sultanov, Naheed Nazrui, Aminur Rahman, Janine De Zeeuw, Jaap Koot, Arathi P. Rao, Keerthana Prasad, Guruvare Shyamala, Premalatha Siddharta, Jelle Stekelenburg, Jogchum Jan Beltman

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Introduction: Visual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could address these limitations. We conducted a diagnostic study to assess the diagnostic performance using visual inspection with acetic acid under magnification of healthcare workers, experts, and an artificial intelligence algorithm. Methods: A total of 22 healthcare workers, 9 gynecologists/experts in visual inspection with acetic acid, and the algorithm assessed a set of 83 images from existing datasets with expert consensus as the reference. Their diagnostic performance was determined by analyzing sensitivity, specificity, and area under the curve, and intra- and inter-observer agreement was measured using Fleiss kappa values. Results: Sensitivity, specificity, and area under the curve were, respectively, 80.4%, 80.5%, and 0.80 (95% CI 0.70 to 0.90) for the healthcare workers, 81.6%, 93.5%, and 0.93 (95% CI 0.87 to 1.00) for the experts, and 80.0%, 83.3%, and 0.84 (95% CI 0.75 to 0.93) for the algorithm. Kappa values for the healthcare workers, experts, and algorithm were 0.45, 0.68, and 0.63, respectively. Conclusion: This study enabled simultaneous assessment and demonstrated that expert consensus can be an alternative to histopathology to establish a reference standard for further training of healthcare workers and the artificial intelligence algorithm to improve diagnostic accuracy.

Original languageEnglish
Article numberijgc-2023-004397
Pages (from-to)1515-1521
Number of pages7
JournalInternational Journal of Gynecological Cancer
Issue number10
Publication statusPublished - 01-10-2023

All Science Journal Classification (ASJC) codes

  • Oncology
  • Obstetrics and Gynaecology


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