Diagnostic Accuracy of Artificial Intelligence Software in Intraoral, Panoramic, and CBCT Imaging: A Pilot Study

Background/Objectives: Artificial intelligence (AI) is increasingly used in dental radiology, but its performance may depend on the imaging modality and the analytical model. This pilot (feasibility) study evaluated the diagnostic accuracy of AI-based software (Diagnocat) in intraoral, panoramic, and cone-beam computed tomography (CBCT) images against a single-expert reference standard, accounting both for indications of pathology and for indications of its absence.

Methods

In this retrospective analysis, 45 anonymized examinations (15 intraoral radiographs, 15 panoramic radiographs, and 15 CBCT) were assessed. The reference standard was established by an expert in dental and maxillofacial radiology with over 30 years of experience, who took no part in selecting the examinations and was blinded to the software output. The primary analysis was performed per diagnostic indication, defined as the combination of one examination, one diagnostic category, and one anatomical location. A secondary binary "pathology versus no pathology" analysis was performed with one observation per tooth or anatomical site. Because observations were clustered within examinations and patients, the analysis was descriptive; no hypothesis tests were performed, and no confidence intervals were reported.