Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs

Szabó V., Szabo B. T., ORHAN K., Veres D. S., Manulis D., Ezhov M., ...More

Journal of Dentistry, vol.147, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 147
  • Publication Date: 2024
  • Doi Number: 10.1016/j.jdent.2024.105105
  • Journal Name: Journal of Dentistry
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, CAB Abstracts, CINAHL, Communication Abstracts, Metadex, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: Artificial intelligence, Deep learning, Dental caries, Dental digital radiography, Diagnostic imaging, Machine learning
  • Ankara University Affiliated: Yes


Objectives: This study aimed to assess the reliability of AI-based system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs. Methods: 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. Results: 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. Conclusions: The Diagnocat CNN supports the evaluation of intraoral radiographs for caries diagnosis, as determined by consensus between human and AI system observers. Clinical significance: 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.