Evaluation of artificial intelligence for detecting impacted third molars on cone-beam computed tomography scans

Abstract

Purpose: The aim of this study was to evaluate the diagnostic performance of artificial intelligence (AI) application evaluating of the impacted third molar teeth in Cone-beam Computed Tomography (CBCT) images.

Material and methods: In total, 130 third molar teeth (65 patients) were included in this retrospective study. Impaction detection, Impacted tooth numbers, root/canal numbers of teeth, relationship with adjacent anatomical structures (inferior alveolar canal and maxillary sinus) were compared between the human observer and AI application. Recorded parameters agreement between the human observer and AI application based on the deep-CNN system was evaluated using the Kappa analysis.

Results: In total, 112 teeth (86.2%) were detected as impacted by AI. The number of roots was correctly determined in 99 teeth (78.6%) and the number of canals in 82 teeth (68.1%). There was a good agreement in the determination of the inferior alveolar canal in relation to the mandibular impacted third molars (kappa: 0.762) as well as the number of roots detection (kappa: 0.620). Similarly, there was an excellent agreement in relation to maxillary impacted third molar and the maxillary sinus (kappa: 0.860). For the maxillary molar canal number detection, a moderate agreement was found between the human observer and AI examinations (kappa: 0.424).

Conclusions: Artificial Intelligence (AI) application showed high accuracy values in the detection of impacted third molar teeth and their relationship to anatomical structures.

Keywords: Artificial intelligence; Deep learning; Impacted third molar; Mandibular canal.