# Bio-inspired neutrosophic-enzyme intelligence framework for pediatric dental disease detection using multi-modal clinical data

## TL;DR

A new AI framework improves pediatric dental disease detection by combining biological principles and uncertainty modeling, offering higher accuracy and efficiency than existing methods.

## Contribution

A novel bio-inspired neutrosophic-enzyme intelligence framework for pediatric dental diagnostics with uncertainty quantification and multi-modal data integration.

## Key findings

- The framework achieved 97.3% diagnostic accuracy, outperforming conventional methods and deep learning models.
- It showed consistent performance across ethnic groups with no demographic bias and reduced diagnostic time by 37.5%.
- Economic analysis revealed a 34.5% cost reduction and 8.7-month return on investment.

## Abstract

Pediatric oral diseases affect over 60% of children globally, yet current diagnostic approaches lack precision and speed necessary for early intervention. This study developed a novel bio-inspired neutrosophic-enzyme intelligence framework integrating biological principles with uncertainty quantification for enhanced pediatric dental diagnostics. We validated the framework across 18,432 pediatric patients aged 3–17 years from six international centers using multi-modal data, including clinical examinations, radiographic imaging, genetic biomarkers, and behavioral assessments. The framework incorporates neutrosophic deep learning for uncertainty modeling, enzyme-inspired feature extraction mimicking salivary enzyme dynamics, axolotl-regenerative healing prediction, and genetic-immunological optimization. Comprehensive validation employed stratified cross-validation, leave-one-center-out testing, and 18-month longitudinal tracking with mixed-effects statistical analysis. The framework achieved 97.3% diagnostic accuracy (95% CI: 95.8–98.2%), 94.7% sensitivity for incipient caries detection, and 96.2% specificity, significantly outperforming conventional methods (80.2% accuracy, p < 0.001) and state-of-art deep learning (89.4% accuracy, p < 0.001). Clinical efficiency improved with 37.5% diagnostic time reduction and 58.1% patient throughput increase. Cross-population validation showed consistent performance (89.7–93.8% accuracy) across ethnic groups with no demographic bias (p > 0.05). Economic analysis demonstrated 34.5% cost reduction with $12,450 per quality-adjusted life year and 8.7-month return on investment. The framework provides explicit uncertainty quantification enabling risk-stratified clinical decisions while maintaining robust safety profiles with zero serious adverse events. All algorithmic implementations and supplementary statistical validation reports are publicly provided to ensure transparency and reproducibility. This bio-inspired approach establishes new benchmarks for AI-assisted pediatric healthcare, demonstrating superior diagnostic performance, clinical efficiency, and global scalability for addressing pediatric oral health disparities.

The online version contains supplementary material available at 10.1038/s41598-025-21923-5.