Impact of Imaging Modality on AI-Based Detection of Incidental Maxillary Sinus Pathology: Comparison of Panoramic...

Background/Objectives: The objective of our study was to compare the diagnostic performance of a popular, commercial dental artificial intelligence (AI) platform (Diagnocat, DGNCT LLC, Miami, FL, USA) for detecting maxillary sinus abnormalities on paired panoramic radiographs (OPG) and cone-beam computed tomography (CBCT) acquired in the same patients, and to examine whether lesion conspicuity predicts correct AI decisions. Methods: In this retrospective paired study, 166 patients contributed 332 maxillary sinuses with OPG and CBCT performed ≤30 days apart. The reference standard was consensus CBCT reading by two observers with third-reader arbitration. The index test was the AI's sinus-level binary output (any abnormality). Accuracy, precision, recall, and F1 score were estimated with patient-clustered 95% bootstrap CIs; secondary analyses assessed category-specific performance and the effect of mucosal thickness and polyp/cyst volume. Results: Our evaluation showed that the platform's performance depended on modality. On CBCT, the accuracy was 69.88% (64.76-74.70%), precision was 87.83% (81.58-93.33%), recall was 54.01% (46.74-61.17%), and F1 score was 66.89% (60.34-72.84%). On OPG, the accuracy was 50.6% (44.58-55.41%), precision was 67.80% (55.38-79.66%), recall was 21.39% (15.62-27.32%), and F1 score 32.52% (24.79-39.69%). On CBCT, higher mucosal thickness and larger polyp/cyst volume strongly predicted correct AI calls; no such effect was seen with OPG. Conclusions: In conclusion, the evaluated AI showed high precision but only moderate recall on CBCT and unreliable performance on OPG. Outputs must be interpreted by a professional; AI alerts on OPG should not guide management without CBCT confirmation.