The Influence of Panoramic Radiograph Quality on the Accuracy of AI-Based Tooth Detection

Objectives: This study aimed to evaluate the influence of panoramic radiograph quality on the performance of an artificial intelligence (AI)-based tooth detection system and to identify specific image quality criteria associated with detection accuracy. Methods: A total of 424 panoramic radiographs were retrospectively selected from a clinical database. Radiographic quality was assessed using a modified Clinical Image Evaluation Chart, including criteria related to bite block presence, anteroposterior positioning, occlusal plane curvature, patient movement, anatomical coverage, overlapping contact points, air gap, contrast, cervical spine overlap, symmetry of the ascending mandibular ramus, and the number of visible teeth. Automated tooth detection was performed using a convolutional neural network based on the Mask R-CNN architecture (SynbrAIn, Italy). AI detection outputs were validated against expert human evaluation. Spearman’s rank correlation analyses were conducted to assess associations between individual image quality criteria and the number of AI detection errors per radiograph. Results: Significant negative associations were observed between AI detection errors and the number of visible teeth (ρ = −0.311, p < 0.001), presence of a bite block (ρ = −0.248, p < 0.001), reduced patient movement (ρ = −0.204, p < 0.001), correct anteroposterior positioning (ρ = −0.165, p < 0.001), and overall image quality score (ρ = −0.120, p = 0.010). In contrast, the presence of an air gap above the anterior teeth (ρ = 0.099, p = 0.042) and overlapping contact points (ρ = 0.122, p = 0.012) were positively associated with increased detection errors. No significant associations were identified for occlusal plane curvature, contrast, cervical spine overlap, anatomical coverage, or mandibular ramus symmetry. Overall, the AI system was more sensitive to indicators of anatomical completeness and patient positioning than to minor radiographic imperfections. Conclusions: Panoramic radiograph quality, particularly indicators of anatomical completeness and patient positioning, is associated with the performance of AI-based tooth detection. While the AI system demonstrated robustness to common image quality variations, adherence to standardized acquisition protocols remains important to minimize detection errors.