{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T16:58:56Z","timestamp":1791910736363,"version":"4.3.4"},"reference-count":35,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T00:00:00Z","timestamp":1768608000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Dental Journal"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.identj.2026.109437","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:20:10Z","timestamp":1775578810000},"page":"109437","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"title":["EnamelNet-TRiX: A Lesion-Aware Dual-Transformer With Cross-Attention for Early and Advanced Enamel Caries Diagnosis"],"prefix":"10.1016","volume":"76","author":[{"given":"Sastika","family":"Balachandran","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raja","family":"Marappan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.identj.2026.109437_bib0001","article-title":"Caries-Spectra: a dataset of enamel caries","volume":"V2","author":"Shahriar","year":"2023","journal-title":"Mendeley Data"},{"issue":"8","key":"10.1016\/j.identj.2026.109437_bib0002","doi-asserted-by":"crossref","first-page":"911","DOI":"10.3390\/bioengineering10080911","article-title":"Deep learning-based recognition of periodontitis and dental caries in dental X-ray images","volume":"10","author":"Chen","year":"2023","journal-title":"Bioengineering"},{"issue":"2","key":"10.1016\/j.identj.2026.109437_bib0003","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.identj.2023.10.003","article-title":"Dental caries detection and classification in CBCT images using deep learning","volume":"74","author":"Esmaeilyfard","year":"2024","journal-title":"Int Dent J"},{"issue":"2","key":"10.1016\/j.identj.2026.109437_bib0004","doi-asserted-by":"crossref","first-page":"226","DOI":"10.3390\/diagnostics13020226","article-title":"An explainable deep learning model to prediction dental caries using panoramic radiograph images","volume":"13","author":"Oztekin","year":"2023","journal-title":"Diagnostics"},{"issue":"6","key":"10.1016\/j.identj.2026.109437_bib0005","doi-asserted-by":"crossref","first-page":"3146","DOI":"10.1007\/s10278-024-01113-x","article-title":"AI-assisted detection of interproximal, occlusal, and secondary caries on bite-wing radiographs: a single-shot deep learning approach","volume":"37","author":"Karaku\u015f","year":"2024","journal-title":"J Digit Imaging"},{"key":"10.1016\/j.identj.2026.109437_bib0006","doi-asserted-by":"crossref","first-page":"18320","DOI":"10.1109\/ACCESS.2022.3150358","article-title":"Dental caries detection using score-based multi-input deep convolutional neural network","volume":"10","author":"Imak","year":"2022","journal-title":"IEEE Access"},{"issue":"22","key":"10.1016\/j.identj.2026.109437_bib0007","doi-asserted-by":"crossref","first-page":"3471","DOI":"10.3390\/diagnostics13223471","article-title":"Evaluation of a decision support system developed with deep learning approach for detecting dental caries with cone-beam computed tomography imaging","volume":"13","author":"Amasya","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.identj.2026.109437_bib0008","doi-asserted-by":"crossref","first-page":"105105","DOI":"10.1016\/j.jdent.2024.105105","article-title":"Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs","volume":"147","author":"Szab\u00f3","year":"2024","journal-title":"J Dent"},{"issue":"1","key":"10.1016\/j.identj.2026.109437_bib0009","doi-asserted-by":"crossref","first-page":"16807","DOI":"10.1038\/s41598-021-96368-7","article-title":"Deep learning for early dental caries detection in bitewing radiographs","volume":"11","author":"Lee","year":"2021","journal-title":"Sci Rep"},{"issue":"3","key":"10.1016\/j.identj.2026.109437_bib0010","doi-asserted-by":"crossref","first-page":"e889","DOI":"10.1002\/cre2.889","article-title":"Evaluation of the effects of postprocessing settings in digital bitewing radiographs on proximal caries detection","volume":"10","author":"Abdinian","year":"2024","journal-title":"Clin Exp Dent Res"},{"key":"10.1016\/j.identj.2026.109437_bib0011","doi-asserted-by":"crossref","first-page":"4641","DOI":"10.1038\/s41598-024-84737-x","article-title":"Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8","volume":"15","author":"Bayati","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.identj.2026.109437_bib0012","first-page":"291","article-title":"A survey on dental disease detection based on deep learning algorithm performance using various radiographs","author":"Dhake","year":"2022","journal-title":"Proc Int Conf Adv Sci Technol (ICAST)"},{"issue":"9","key":"10.1016\/j.identj.2026.109437_bib0013","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.3390\/diagnostics11091672","article-title":"Deep learning for caries detection and classification","volume":"11","author":"Lian","year":"2021","journal-title":"Diagnostics"},{"issue":"1","key":"10.1016\/j.identj.2026.109437_bib0014","doi-asserted-by":"crossref","first-page":"102077","DOI":"10.1016\/j.jebdp.2024.102077","article-title":"Artificial intelligence platforms in dental caries detection: A systematic review and meta-analysis","volume":"25","author":"Abbott","year":"2025","journal-title":"J Evid Based Dent Pract"},{"key":"10.1016\/j.identj.2026.109437_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109221","article-title":"Enhancing dental caries classification in CBCT images by using image processing and self-supervised learning","volume":"183","author":"Zanini","year":"2024","journal-title":"Comput Biol Med"},{"issue":"6","key":"10.1016\/j.identj.2026.109437_bib0016","doi-asserted-by":"crossref","first-page":"690","DOI":"10.3390\/children11060690","article-title":"Automatic tooth caries segmentation in panoramic radiographs of children in primary, mixed, and permanent dentition","volume":"11","author":"Asci","year":"2024","journal-title":"Children"},{"key":"10.1016\/j.identj.2026.109437_bib0017","doi-asserted-by":"crossref","first-page":"38945","DOI":"10.1109\/ACCESS.2025.3539844","article-title":"DentC_BSFL Net: boundary refined and spatial adaptive feature learning network for enhanced dental caries segmentation","volume":"13","author":"Neeraja","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.identj.2026.109437_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2024.105666","article-title":"CrossViT with ECAP: enhanced deep learning for jaw lesion classification","volume":"193","author":"Panyarak","year":"2025","journal-title":"Int J Med Inform"},{"key":"10.1016\/j.identj.2026.109437_bib0019","doi-asserted-by":"crossref","first-page":"24612","DOI":"10.1109\/ACCESS.2025.3554184","article-title":"EFFResNet-ViT: a fusion-based convolutional and vision transformer model for explainable medical image classification","volume":"13","author":"Hussain","year":"2025","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.identj.2026.109437_bib0020","doi-asserted-by":"crossref","first-page":"987","DOI":"10.3390\/cancers16050987","article-title":"Classification of mobile-based oral cancer images using the vision transformer and the Swin transformer","volume":"16","author":"Song","year":"2024","journal-title":"Cancers"},{"key":"10.1016\/j.identj.2026.109437_bib0021","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1186\/s12903-025-05431-6","article-title":"Enhancing furcation involvement classification on panoramic radiographs with vision transformers","volume":"25","author":"Zhang","year":"2025","journal-title":"BMC Oral Health"},{"key":"10.1016\/j.identj.2026.109437_bib0022","article-title":"MPGLNet: multi-scale progressive global\u2013local fusion dental point cloud complementation network","volume":"111","author":"Zhang","year":"2026","journal-title":"Biomed Sign Proc Control"},{"key":"10.1016\/j.identj.2026.109437_bib0023","doi-asserted-by":"crossref","unstructured":"Li X., Ma X., Zhao Y., Hu J., Liu J., Yang Z., Han F., Zhang J., Liu W., Zhou Z. A Multi-center dental panoramic radiography image dataset for impacted teeth, periodontitis, and dental caries: Benchmarking segmentation and classification tasks. J Imaging Inform Med. 2024;37(2):831\u201341. https:\/\/doi.org\/10.1007\/s10278-024-00972-8.","DOI":"10.1007\/s10278-024-00972-8"},{"key":"10.1016\/j.identj.2026.109437_bib0024","doi-asserted-by":"crossref","first-page":"14201","DOI":"10.1109\/ACCESS.2024.3472012","article-title":"PDCNET: deep convolutional neural network for classification of periodontal disease using dental radiographs","volume":"12","author":"Bilal","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.identj.2026.109437_bib0025","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1038\/s41597-024-04306-9","article-title":"A multi-modal dental dataset for semi-supervised deep learning image segmentation","volume":"12","author":"Wang","year":"2025","journal-title":"Sci Data"},{"issue":"2","key":"10.1016\/j.identj.2026.109437_bib0026","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.identj.2025.02.005","article-title":"The transformative role of artificial intelligence in dentistry: a comprehensive overview. Part 1: fundamentals of AI and its contemporary applications in dentistry","volume":"75","author":"Samaranayake","year":"2025","journal-title":"Int Dent J"},{"key":"10.1016\/j.identj.2026.109437_bib0027","doi-asserted-by":"crossref","unstructured":"Tuygunov N., Samaranayake L., Khurshid Z., Rewthamrongsris P., Schwendicke F., Osathanon T., Yahya N.A., The transformative role of artificial intelligence in dentistry: A comprehensive overview Part 2: The promise and perils, and the international dental federation communique, Int Dent J, 75, 2, 2025, 397-404, ISSN 0020-6539, https:\/\/doi.org\/10.1016\/j.identj.2025.02.006.","DOI":"10.1016\/j.identj.2025.02.006"},{"issue":"6","key":"10.1016\/j.identj.2026.109437_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.identj.2025.103931","article-title":"AI-driven CBCT analysis for surgical decision-making and mucosal damage prediction in sinus lift surgery for patients with low RBH","volume":"75","author":"Deng","year":"2025","journal-title":"Int Dent J"},{"issue":"1","key":"10.1016\/j.identj.2026.109437_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.identj.2025.109322","article-title":"Artificial intelligence in dentistry: a concise review of reporting checklists and guidelines","volume":"76","author":"Khurshid","year":"2025","journal-title":"Int Dent J"},{"issue":"5","key":"10.1016\/j.identj.2026.109437_bib0030","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1016\/j.prosdent.2024.09.014","article-title":"Application of 3D neural networks and explainable AI to classify ICDAS detection system on mandibular molars","volume":"133","author":"Farook","year":"2025","journal-title":"J Prosthet Dent"},{"issue":"7","key":"10.1016\/j.identj.2026.109437_bib0031","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1007\/s12652-024-04795-x","article-title":"AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping","volume":"15","author":"Inani","year":"2024","journal-title":"J Ambient Intell Human Comput"},{"key":"10.1016\/j.identj.2026.109437_bib0032","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1186\/s12903-022-02589-1","article-title":"Caries detection with tooth surface segmentation on intraoral photographic images","volume":"22","author":"Park","year":"2022","journal-title":"BMC Oral Health"},{"issue":"24","key":"10.1016\/j.identj.2026.109437_bib0033","doi-asserted-by":"crossref","first-page":"8959","DOI":"10.3390\/jcm14248959","article-title":"Automated classification of enamel caries from intraoral images using deep learning models: a diagnostic study","volume":"14","author":"Asiri","year":"2025","journal-title":"J Clin Med"},{"key":"10.1016\/j.identj.2026.109437_bib0034","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1186\/s12903-025-05419-2","article-title":"Early childhood caries risk prediction using machine learning approaches in Bangladesh","volume":"25","author":"Hasan","year":"2025","journal-title":"BMC Oral Health"},{"issue":"4","key":"10.1016\/j.identj.2026.109437_bib0035","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.26599\/BDMA.2024.9020027","article-title":"Gfdet: multi-level feature fusion network for caries detection using dental endoscope images","volume":"7","author":"Gao","year":"2024","journal-title":"Big Data Min Anal"}],"container-title":["International Dental Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S002065392600033X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S002065392600033X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:23:43Z","timestamp":1787847823000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S002065392600033X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["S002065392600033X"],"URL":"https:\/\/doi.org\/10.1016\/j.identj.2026.109437","relation":{},"ISSN":["0020-6539"],"issn-type":[{"value":"0020-6539","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"EnamelNet-TRiX: A Lesion-Aware Dual-Transformer With Cross-Attention for Early and Advanced Enamel Caries Diagnosis","name":"articletitle","label":"Article Title"},{"value":"International Dental Journal","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.identj.2026.109437","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Inc. on behalf of FDI World Dental Federation.","name":"copyright","label":"Copyright"}],"article-number":"109437"}}