MultiDentNet: a unified deep learning framework for multi-class dental condition screening and preliminary oral lesion triage.

To develop and evaluate MultiDentNet, a unified deep learning framework for multi-class dental condition screening and preliminary risk stratification of cancer-suspicious oral lesions, utilizing backbone-diverse ensembling and inter-class relational modeling. Four complementary CNN backbones (DenseNet-121, EfficientNetV2-S, ResNet-50, Inception-V3) integrated with Squeeze-and-Excitation (SE) attention, graph convolutional networks (GCNs), and multi-task learning were fused via validation-optimized weighting. The framework was evaluated on 10,235 clinical intraoral images (five conditions) and 940 clinically labeled oral lesion images (cancer-suspicious vs. non-cancer-suspicious; histopathological confirmation unavailable). Performance was assessed using accuracy, Cohen's κ, Matthews correlation coefficient (MCC), false negative rate (FNR), and bootstrapped 95% confidence intervals (CIs), alongside simulated domain-shift robustness testing. The ensemble achieved 99.70% accuracy (95% CI 99.32-100.00%) for dental classification ([Formula: see text]) and 95.71% (95% CI 92.20-98.58%) for oral lesion risk stratification ([Formula: see text]). For cancer-suspicious lesions, recall reached 97.31% (FNR: 2.69%, 95% CI 0.00-7.15%). Architectural diversity successfully mitigated class imbalance, significantly reducing the Hypodontia FNR to 1.65% (95% CI 0.00-4.24%) compared to single-model baselines. Performance demonstrated moderate resilience to acquisition variability (Δaccuracy ≈ 8-12% degradation under brightness and contrast perturbations) but degraded substantially under high-frequency noise. Grad-CAM visualizations localized attention to clinically relevant morphological features. MultiDentNet provides an interpretable, efficient baseline for dental screening and lesion triage. Serving as an adjunctive proof-of-concept, its metrics reflect an upper bound due to reliance on single-center, clinically labeled data. Prospective multi-center validation with histopathological standards remains necessary. Code: https://github.com/Aliyar4061/MultiDentNetV3 . Positioned as an adjunctive triage tool for resource-constrained settings, high-volume workflows, and tele-dentistry, designed to augment clinician-in-the-loop oversight.