TY - JOUR
T1 - Artificial intelligence and visual inspection in cervical cancer screening
AU - Nakisige, Carolyn
AU - de Fouw, Marlieke
AU - Kabukye, Johnblack
AU - Sultanov, Marat
AU - Nazrui, Naheed
AU - Rahman, Aminur
AU - de Zeeuw, Janine
AU - Koot, Jaap
AU - Rao, Arathi P
AU - Prasad, Keerthana
AU - Shyamala, Guruvare
AU - Siddharta, Premalatha
AU - Stekelenburg, Jelle
AU - Beltman, Jogchum Jan
N1 - © IGCS and ESGO 2023. Re-use permitted under CC BY-NC. No commercial re-use. Published by BMJ.
PY - 2023/10
Y1 - 2023/10
N2 - 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.
AB - 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.
U2 - 10.1136/ijgc-2023-004397
DO - 10.1136/ijgc-2023-004397
M3 - Article
C2 - 37666527
SN - 1048-891X
VL - 33
SP - 1515
EP - 1521
JO - International Journal of Gynecological Cancer
JF - International Journal of Gynecological Cancer
ER -