Bio-inspired neutrosophic-enzyme intelligence framework for pediatric dental disease detection using multi-modal...

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Healthcare
• Pediatric Dentistry

Background:

• Pediatric oral diseases are prevalent globally, affecting over 60% of children.
• Current diagnostic methods for pediatric dental conditions lack the precision and speed required for timely intervention.
• There is a critical need for advanced diagnostic tools to improve early detection and management of oral health issues in children.

Purpose of the Study:

• To develop and validate a novel bio-inspired neutrosophic-enzyme intelligence framework for enhanced pediatric dental diagnostics.
• To integrate biological principles with uncertainty quantification for improved diagnostic accuracy and efficiency.
• To assess the framework's performance, clinical efficiency, cost-effectiveness, and global scalability.

Main Methods:

• Development of a bio-inspired framework combining neutrosophic deep learning, enzyme-inspired feature extraction, axolotl-regenerative healing prediction, and genetic-immunological optimization.
• Validation across 18,432 pediatric patients (ages 3-17) using multi-modal data (clinical, radiographic, genetic, behavioral).
• Rigorous statistical validation including stratified cross-validation, leave-one-center-out testing, and 18-month longitudinal tracking.

Main Results:

• Achieved 97.3% diagnostic accuracy, significantly outperforming conventional (80.2%) and deep learning (89.4%) methods (p < 0.001).
• Demonstrated high sensitivity (94.7%) for incipient caries detection and specificity (96.2%).
• Improved clinical efficiency by reducing diagnostic time by 37.5% and increasing patient throughput by 58.1%, with significant cost reductions and rapid ROI.

Conclusions:

• The novel bio-inspired framework offers superior diagnostic performance and clinical efficiency for pediatric dental care.
• The system provides explicit uncertainty quantification, enabling risk-stratified clinical decisions with a robust safety profile.
• This AI-driven approach establishes new benchmarks for global pediatric oral healthcare, addressing health disparities effectively.