Inter observer variability among gynecologists in manual cervix image analysis for detection of cervical epithelial abnormalities

Kudva, Vidya; Guruvare, Shyamala; Prasad, Keerthana; Kulkarni, Kiran Abhijit; Premalatha, T. S.; Kamath, Asha; Nair, Suma; Rao, Chythra R

doi:10.1016/j.cegh.2019.05.009

Cited by 2 publications

(1 citation statement)

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“…We determined the intra-observer and inter-observer agreement within teams of healthcare workers, experts, and the algorithm using Fleiss κ values. 32 Activities under the PRESCRIP-TEC were approved by the institutional review boards of Bangladesh, India, and Uganda and written informed consent obtained from both the healthcare workers and experts.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence and visual inspection in cervical cancer screening

Nakisige,

de Fouw,

Kabukye

et al. 2023

Int J Gynecol Cancer

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IntroductionVisual 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.MethodsA 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.ResultsSensitivity, 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.ConclusionThis 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.

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Section: Methodsmentioning

confidence: 99%