ArticleScientific reports2025
DentoMorph-LDMs: diffusion models based on novel adaptive 8-connected gum tissue and deciduous teeth loss for dental image augmentation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fixture design effects on posterior dental implant stability using finite element analysis (FEA): a systematic review.Head & face medicine · 2025Pooled it
- Enhanced cybersecurity threat detection using novel tri-metaheuristic loss functions in generative adversarial networks with adaptive attention preservation for network traffic augmentation.Scientific reports · 2026Article
- Enhanced paddy leaf disease detection using novel dual metaheuristic loss functions in generative adversarial networks with identity block preservation for thermal image augmentation.Scientific reports · 2026Article
- A novel Pulp Caries GAN multi loss GAN with new pulp inspired metaheuristics for pediatric dental caries detection and segmentation.Scientific reports · 2026Article
- Novel metaheuristic optimized latent diffusion framework for automated oral disease detection in public health screening.Scientific reports · 2025Article
- Adaptive identity-regularized generative adversarial networks with species-specific loss functions for enhanced fish classification and segmentation through data augmentation.Scientific reports · 2025Article
- Bio-inspired neutrosophic-enzyme intelligence framework for pediatric dental disease detection using multi-modal clinical data.Scientific reports · 2025Article
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4 authors.
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Abstract
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.
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