Geometric Distortion Induced by Vertical Camera Positioning in Dental Imaging: Toward 2D-3D Reconstruction and AI-Driven Workflows

This study quantified projection-dependent geometric distortion induced by vertical camera angulation in two-dimensional (2D) dental image acquisition and evaluated its implications for integration with three-dimensional (3D) CAD/CAM and artificial intelligence (AI)-driven workflows. To our knowledge, this study is among the first to use quantitative methods to characterize projection-induced distortion across the dental arch as a function of vertical camera angulation. Fourteen fully dentate casts were photographed at nine standardized vertical angulations using a controlled acquisition setup based on the standardized occlusal plane angle (SOPA). Tooth surface areas were measured through digital tracing and analyzed with a mixed-effects model (α = 0.05). Significant associations were identified between vertical camera angulation and measured tooth surface area for all teeth except canines (p < 0.05 for all except canines). Anterior teeth demonstrated increased apparent surface area at superior camera angulations, whereas posterior teeth were more prominently represented at inferior angulations. Central incisors, lateral incisors, and first premolars exhibited maximal visibility above the occlusal plane, while second premolars and molars were more optimally visualized below it. These findings indicate that vertical camera angulation induces non-uniform, region-specific geometric distortion across the dental arch. From a computational perspective, these distortions represent a systematic source of variability in 2D photographic datasets used in CAD/CAM workflows, virtual smile design, and AI-assisted image analysis. Because modern machine learning systems depend on geometrically consistent input data, uncorrected projection-induced distortion may reduce the reliability and generalizability of downstream algorithmic outputs. Accordingly, the present findings establish a quantitative basis for recognizing projection-induced variability in 2D dental photographs and support future development of geometry-aware calibration strategies for 2D-3D digital integration. AI-assisted correction represents a future translational direction contingent upon explicit alignment between acquisition geometry, image formation, and computational modeling.