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

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

SUBMITTER: Marie HS 

PROVIDER: S-EPMC12534394 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature