DentoMorph-LDMs: diffusion models based on novel adaptive 8-connected gum tissue and deciduous teeth loss for dental image augmentation

Pediatric dental image analysis faces critical challenges in disease detection due to missing or corrupted pixel regions and the unique developmental characteristics of deciduous teeth, with current Latent Diffusion Models (LDMs) failing to preserve anatomical integrity during reconstruction of pediatric oral structures. We developed two novel biologically-inspired loss functions integrated within LDMs specifically designed for pediatric dental imaging: Gum-Adaptive Pixel Imputation (GAPI) utilizing adaptive 8-connected pixel neighborhoods that mimic pediatric gum tissue adaptive behavior, and Deciduous Transition-Based Reconstruction (DTBR) incorporating developmental stage awareness based on primary teeth transition patterns observed in children aged 2-12 years. These algorithms guide the diffusion process toward developmentally appropriate reconstructions through specialized loss functions that preserve structural continuity of deciduous dentition and age-specific anatomical features crucial for accurate pediatric diagnosis. Experimental validation on 2,255 pediatric dental images across six conditions (caries, calculus, gingivitis, tooth discoloration, ulcers, and hypodontia) demonstrated superior image generation performance with Inception Score of 9.87, Fréchet Inception Distance of 4.21, Structural Similarity Index of 0.952, and Peak Signal-to-Noise Ratio of 34.76, significantly outperforming eleven competing diffusion models. Pediatric disease detection using enhanced datasets achieved statistically significant improvements across five detection models: +0.0694 in mean Average Precision [95% CI: 0.0608-0.0780], + 0.0606 in Precision [0.0523-0.0689], + 0.0736 in Recall [0.0651-0.0821], and + 0.0678 in F1-Score [0.0597-0.0759] (all p < 0.0001), enabling pediatric dentists to detect early-stage caries, developmental anomalies, and eruption disorders with unprecedented accuracy. This framework revolutionizes pediatric dental diagnosis by providing pediatric dentists with AI-enhanced imaging tools that account for the unique biological characteristics of developing dentition, significantly improving early detection of oral diseases in children and establishing a foundation for age-specific dental AI applications that enhance clinical decision-making in pediatric dental practice.