Novel metaheuristic optimized latent diffusion framework for automated oral disease detection in public health...

Automated oral disease detection systems face significant challenges from degraded radiographic imaging quality and limited pathological training data, particularly for rare conditions in public health screening environments. We introduce DentoSMART-LDM, the first framework to integrate metaheuristic optimization with latent diffusion models for dental imaging, featuring a novel Dynamic Self-Adaptive Multi-objective Metaheuristic Algorithm for Radiographic Tooth enhancement (DSMART) combined with a specialized pathology-aware Latent Diffusion Model (DentoLDM). Our pioneering DSMART algorithm represents the first metaheuristic approach specifically designed for dental radiographic enhancement, treating optimization as a multi-objective problem that simultaneously balances five dental quality indices through adaptive search mechanisms, while our innovative DentoLDM introduces the first pathology-specific attention mechanisms that preserve diagnostic integrity during synthetic data generation. This groundbreaking dual-component architecture addresses both image degradation and data scarcity simultaneously - a capability unprecedented in existing dental AI systems. For the first time in dental imaging research, we demonstrate adaptive optimization that dynamically adjusts processing intensity based on anatomical characteristics including bone density variations, soft tissue artifacts, and metallic restoration interference. Evaluated on the OralPath Dataset comprising 25,000 high-resolution dental radiographs across 12 pathological conditions with comprehensive external validation across seven independent clinical datasets (82,300 images), DentoSMART-LDM achieved superior performance with SSIM of 0.941 ± 0.023 and PSNR of 34.82 ± 1.47 dB, representing statistically significant improvements of 9.0% and 11.5% respectively compared to competing methods (p < 0.001). Diagnostic models trained on DentoSMART-LDM enhanced datasets achieved 97.3 ± 0.18% overall accuracy (95% CI: 97.09-97.51%), maintaining 87.7 ± 0.8% average accuracy across diverse clinical settings under natural class imbalance conditions. Blinded expert assessment by 20 board-certified oral pathologists revealed significant improvements in diagnostic accuracy (+ 17.4%, 95% CI: 15.8-19.0%) and expert confidence (+ 23.4%, p < 0.001), while few-shot learning evaluation demonstrated exceptional performance with only 2 samples per pathology (89.2 ± 1.7% accuracy). This novel integration of multi-objective metaheuristic optimization with medical generative models represents a paradigm shift in dental AI, offering the first comprehensive solution that balances enhancement quality, diagnostic preservation, and computational efficiency while providing unprecedented few-shot learning capabilities for rare oral pathologies in underserved communities.