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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月8日

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  • Amira Abdelhafeez Elkhatib,
  • Mostafa Elbaz,
  • Riham Sobhy Soliman,
  • Mona Elshirbini Hafez

摘要

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.