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DentoMorph-LDMs: diffusion models based on novel adaptive 8-connected gum tissue and deciduous teeth loss for dental
Hanaa Salem Marie1, Mostafa Elbaz2, Riham Sobhy Soliman3
1Faculty of Artificial Intelligence, Delta University for Science and Technology, Gamasa, 35712, Egypt. Hana.salem@deltauniv.edu.eg.
Scientific Reports
|July 27, 2025
Summary
New AI models improve pediatric dental image analysis by preserving tooth anatomy and detecting diseases like caries more accurately. These biologically-inspired algorithms enhance early diagnosis for better children's oral health outcomes.
Area of Science:
- Artificial Intelligence in Dentistry
- Pediatric Oral Health Imaging
- Medical Image Analysis
Background:
- Pediatric dental image analysis is challenged by missing data and unique deciduous tooth development.
- Existing Latent Diffusion Models (LDMs) struggle to maintain anatomical integrity in pediatric oral structures.
- Accurate reconstruction is crucial for diagnosing conditions in children's developing dentition.
Purpose of the Study:
- To develop novel, biologically-inspired loss functions for LDMs in pediatric dental imaging.
- To improve the reconstruction of pediatric oral structures, preserving anatomical and developmental accuracy.
- To enhance the performance of AI models in detecting oral diseases in children.
Main Methods:
- Introduced Gum-Adaptive Pixel Imputation (GAPI) using adaptive pixel neighborhoods mimicking gum tissue.
- Developed Deciduous Transition-Based Reconstruction (DTBR) considering primary teeth transition patterns (ages 2-12).
- Integrated GAPI and DTBR into LDMs, validating on 2,255 images across six conditions.
Main Results:
- Achieved superior image generation metrics (IS: 9.87, FID: 4.21, SSIM: 0.952, PSNR: 34.76), outperforming eleven other models.
- Enhanced datasets led to statistically significant improvements in disease detection models (mean AP, Precision, Recall, F1-Score).
- Demonstrated improved accuracy in detecting early-stage caries, developmental anomalies, and eruption disorders.
Conclusions:
- The novel LDMs with GAPI and DTBR preserve structural continuity and age-specific features in pediatric dental images.
- This framework revolutionizes pediatric dental diagnosis by providing AI-enhanced imaging tools for early disease detection.
- Establishes a foundation for age-specific dental AI applications, enhancing clinical decision-making in pediatric dentistry.
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