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. 2023 Oct 2;33(10):1515-1521.
doi: 10.1136/ijgc-2023-004397.

Artificial intelligence and visual inspection in cervical cancer screening

Carolyn Nakisige  1 , Marlieke de Fouw  2 , Johnblack Kabukye  3 , Marat Sultanov  4 , Naheed Nazrui  5 , Aminur Rahman  6 , Janine de Zeeuw  4 , Jaap Koot  4 , Arathi P Rao  7 , Keerthana Prasad  8 , Guruvare Shyamala  9 , Premalatha Siddharta  10 , Jelle Stekelenburg  4 , Jogchum Jan Beltman  11
Affiliations

Artificial intelligence and visual inspection in cervical cancer screening

Carolyn Nakisige et al. Int J Gynecol Cancer. .

Abstract

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.

Keywords: Cervical Cancer.

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Conflict of interest statement

Competing interests: KP and SG were involved in development of the Artificial Intelligence algorithm.

Figures

Figure 1
Figure 1
Flow chart of the VIA dataset assessed, comprising 96 unique images and four duplicates. Three were excluded at an expert consensus meeting, leaving 93. Ten were excluded after AI failed to assess them, leaving 83 images for final assessment. These comprised 48 VIA negative images, 28 VIA positive images, and nine suspected cancers. The nine suspected cancers were added to the VIA positives to make 35 VIA positive images and 48 VIA negatives to enable comparison with the binary outcome of the AI. AI, artificial intelligence; VIA, visual inspection with acetic acid.
See this image and copyright information in PMC

References

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