{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T13:44:32Z","timestamp":1792071872802,"version":"4.4.0"},"reference-count":24,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical &amp; Exp Dental Res"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Oral Potentially Malignant Disorders (OPMDs), including Leukoplakia and Erythroplakia, carry significant risks of malignant transformation. Early differentiation from confounding inflammatory conditions like Oral Lichen Planus (OLP) and Candidiasis is critical yet challenging due to visual similarities. This study develops a robust, interpretable deep learning framework for automated multi\u2010class classification of pre\u2010localized oral lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>A curated dataset of 705 high\u2010resolution images across five categories (Leukoplakia, Erythroplakia, OLP, Candidiasis, and Normal) was utilized. We proposed a Multi\u2010Architecture Weighted Ensemble Framework integrating ResNet\u201050, Xception, and EfficientNet\u2010B0. A stratified patient\u2010level splitting strategy prevented data leakage. Model interpretability and clinical utility were assessed via Gradient\u2010weighted Class Activation Mapping (Grad\u2010CAM) and Decision Curve Analysis (DCA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The ensemble model achieved 91.2% accuracy and a 90.8% macro\u2010averaged F1\u2010score, significantly outperforming individual baselines. The strategy improved OLP detection (F1\u2010score: 0.83), effectively distinguishing it from Leukoplakia. Grad\u2010CAM confirmed the model focuses on pathognomonic lesion features rather than confounding artifacts. DCA suggested a potential theoretical net clinical benefit over default strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This weighted ensemble framework demonstrates high retrospective accuracy and provides transparent visual explanations for the classification of pre\u2010localized oral lesions. However, it must be interpreted strictly as a preliminary proof\u2010of\u2010concept investigation. While the current results suggest potential adjunctive value, extensive external validation, prospective testing, and clinician\u2010in\u2010the\u2010loop studies are strictly necessary to validate its true clinical utility and impact on patient outcomes in primary care settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cre2.70420","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T10:35:34Z","timestamp":1784630134000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Differentiation of Oral Red\u2010White Lesions: An Interpretable Deep Learning Approach Combining Ensemble Architectures and Saliency Maps"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8171-8043","authenticated-orcid":false,"given":"Mahsa","family":"Koochaki","sequence":"first","affiliation":[{"name":"Department of Oral and Maxillofacial Medicine, School of Dentistry Guilan University of Medical Sciences Rasht Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1574-0357","authenticated-orcid":false,"given":"Amirreza","family":"Mousavie","sequence":"additional","affiliation":[{"name":"Student Research Committee, School of Dentistry Guilan University of Medical Sciences Rasht Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maryam","family":"Basirat","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Medicine, School of Dentistry Guilan University of Medical Sciences Rasht Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amirreza","family":"Hendi","sequence":"additional","affiliation":[{"name":"Department of Prosthodontics, Dental Sciences Research Center, School of Dentistry Guilan University of Medical Sciences Rasht 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Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1055\/s-0042-1760300"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2025.106138"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-02802-5"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/dj13090384"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-22560-8"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.4103\/0019-5154.156315"},{"issue":"12","key":"e_1_2_13_8_1","article-title":"Artificial Intelligence in Early Diagnosis and Prevention of Oral Cancer, Asia\u2010Pacific","volume":"9","author":"Hegde S.","year":"2022","journal-title":"Journal of Oncology 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