This study aimed to assess the reliability of AI-based system that assists the healthcare
processes in the diagnosis of caries on intraoral radiographs.The proximal surfaces
of the 323 selected teeth on the intraoral radiographs were evaluated by two independent
observers using an AI-based (Diagnocat) system. The presence or absence of carious
lesions was recorded during Phase 1. After 4 months, the AI-aided human observers
evaluated the same radiographs (Phase 2), and the advanced convolutional neural network
(CNN) reassessed the radiographic data (Phase 3). Subsequently, data reflecting human
disagreements were excluded (Phase 4). For each phase, the Cohen and Fleiss kappa
values, as well as the sensitivity, specificity, positive and negative predictive
values, and diagnostic accuracy of Diagnocat, were calculated.During the four phases,
the range of Cohen kappa values between the human observers and Diagnocat were κ=0.66-1,
κ=0.58-0.7, and κ=0.49-0.7. The Fleiss kappa values were κ=0.57-0.8. The sensitivity,
specificity and diagnostic accuracy values ranged between 0.51-0.76, 0.88-0.97 and
0.76-0.86, respectively.The Diagnocat CNN supports the evaluation of intraoral radiographs
for caries diagnosis, as determined by consensus between human and AI system observers.Our
study may aid in the understanding of deep learning-based systems developed for dental
imaging modalities for dentists and contribute to expanding the body of results in
the field of AI-supported dental radiology..
