Background/Objectives: Our study aimed to determine the accuracy of the artificial
intelligence-based Diagnocat system (DC) in detecting periapical lesions (PL) on panoramic
radiographs (PRs). Methods: 616 teeth were selected from 357 panoramic radiographs,
including 308 teeth with clearly visible periapical radiolucency and 308 without any
periapical lesion. Three groups were generated: teeth with radiographic signs of caries
(Group 1), teeth with coronal restoration (Group 2), and teeth with root canal filling
(Group 3). The PRs were uploaded to the Diagnocat system for evaluation. The performance
of the convolutional neural network in detecting PLs was assessed by its sensitivity,
specificity, and positive and negative predictive values, as well as the diagnostic
accuracy value. We investigated the possible effect of the palatoglossal air space
(PGAS) on the evaluation of the AI tool. Results: DC identified periapical lesions
in 240 (77.9%) cases out of the 308 teeth with PL and detected no PL in 68 (22.1%)
teeth with PL. The AI-based system detected no PL in any of the groups without PL.
The overall sensitivity, specificity, and diagnostic accuracy of DC were 0.78, 1.00,
and 0.89, respectively. Considering these parameters for each group, Group 2 showed
the highest values at 0.84, 1.00, and 0.95, respectively. Fisher’s Exact test showed
that PGAS does not significantly affect (p = 1) the detection of PL in the upper teeth.
The AI-based system showed lower probability values for detecting PL in the case of
central incisors, wisdom teeth, and canines. The sensitivity and diagnostic accuracy
of DC for detecting PL on canines showed lower values at 0.27 and 0.64, respectively.
Conclusions: The CNN-based Diagnocat system can support the diagnosis of PL on PRs
and serves as a decision-support tool during radiographic assessments.
