A novel Pulp Caries GAN multi loss GAN with new pulp inspired metaheuristics for pediatric dental caries detection and segmentation.

Early detection of dental caries in pediatric populations remains challenging due to limited annotated datasets and the subtle manifestation of incipient lesions. This study introduces Pulp-Caries-GAN, a novel generative adversarial network incorporating a biomimetic optimization strategy for high-fidelity synthetic dental image generation. The framework integrates a multi-loss architecture combining adversarial, pixel-wise, perceptual, and structural similarity losses with a pulp-inspired metaheuristic function that models neurophysiological dynamics of dental pulp tissue to preserve anatomical coherence. The optimization strategy employs spatially-adaptive regularization through an anatomical masking mechanism that enforces tissue-specific constraints based on diagnostic importance. Experimental validation was conducted on a pediatric dental panoramic dataset comprising 193 annotated images from 106 patients aged 2-13 years. The results demonstrate superior image synthesis quality compared to conventional GAN architectures, achieving a Fréchet Inception Distance of 154.87, Inception Score of 80.12, and Peak Signal-to-Noise Ratio of 80.04. Integration of synthetic images generated by Pulp-Caries-GAN significantly enhanced segmentation performance across multiple U-Net variants. The Hierarchical Dense U-Net achieved optimal results with a Dice coefficient of 95.12%, accuracy of 95.65%, precision of 95.32%, and recall of 93.7%. Ablation studies confirmed the critical role of the pulp-inspired loss component and anatomical masking in maintaining structural integrity while reducing artifacts in synthetic images. Clinical validation by five board-certified pediatric dentists revealed that 87% of synthetic images were clinically indistinguishable from real radiographs, with 94% of synthetic lesions exhibiting anatomically correct progression patterns. These findings demonstrate the efficacy of biomimetic optimization approaches in medical image synthesis and establish a robust framework for automated pediatric dental caries detection with potential for clinical translation.