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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\u2019s\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\kappa$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , 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\u2013100.00%) for dental classification (\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\kappa = 0.996$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) and 95.71% (95% CI 92.20\u201398.58%) for oral lesion risk stratification (\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\kappa = 0.913$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ). For cancer-suspicious lesions, recall reached 97.31% (FNR: 2.69%, 95% CI 0.00\u20137.15%). Architectural diversity successfully mitigated class imbalance, significantly reducing the Hypodontia FNR to 1.65% (95% CI 0.00\u20134.24%) compared to single-model baselines. Performance demonstrated moderate resilience to acquisition variability (\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\Delta$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    accuracy\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\approx$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    8\u201312% 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:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/Aliyar4061\/MultiDentNetV3\" ext-link-type=\"uri\">https:\/\/github.com\/Aliyar4061\/MultiDentNetV3<\/jats:ext-link>\n                    . Positioned as an adjunctive triage tool for resource-constrained settings, high-volume workflows, and tele-dentistry, designed to augment clinician-in-the-loop oversight.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-52264-6","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T06:55:46Z","timestamp":1778655346000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MultiDentNet: a unified deep learning framework for multi-class dental condition screening and preliminary oral lesion triage"],"prefix":"10.1038","volume":"16","author":[{"given":"Ali Zeydi","family":"Abdian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Masoud","family":"Javidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Najme","family":"Mansouri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farzaneh","family":"Mehranfar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sahar","family":"Cheperli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"52264_CR1","doi-asserted-by":"publisher","unstructured":"Kamran, M., Waseem, M., Shafi, N. & Zahid A. 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