{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T14:57:18Z","timestamp":1779375438015,"version":"3.53.1"},"reference-count":31,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T00:00:00Z","timestamp":1627689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["-"],"award-info":[{"award-number":["-"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Health Department of the State of Rio de Janeiro","award":["-"],"award-info":[{"award-number":["-"]}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["-"],"award-info":[{"award-number":["-"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004586","name":"Funda\u00e7\u00e3o Carlos Chagas Filho de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro","doi-asserted-by":"publisher","award":["-"],"award-info":[{"award-number":["-"]}],"id":[{"id":"10.13039\/501100004586","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011911","name":"Instituto Nacional de Ci\u00eancia e Tecnologia em Medicina Assistida por Computa\u00e7\u00e3o Cient\u00edfica","doi-asserted-by":"publisher","award":["-"],"award-info":[{"award-number":["-"]}],"id":[{"id":"10.13039\/501100011911","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Dental caries is an extremely common problem in dentistry that affects a significant part of the population. Approximal caries are especially difficult to identify because their position makes clinical analysis difficult. Radiographic evaluation\u2014more specifically, bitewing images\u2014are mostly used in such cases. However, incorrect interpretations may interfere with the diagnostic process. To aid dentists in caries evaluation, computational methods and tools can be used. In this work, we propose a new method that combines image processing techniques and convolutional neural networks to identify approximal dental caries in bitewing radiographic images and classify them according to lesion severity. For this study, we acquired 112 bitewing radiographs. From these exams, we extracted individual tooth images from each exam, applied a data augmentation process, and used the resulting images to train CNN classification models. The tooth images were previously labeled by experts to denote the defined classes. We evaluated classification models based on the Inception and ResNet architectures using three different learning rates: 0.1, 0.01, and 0.001. The training process included 2000 iterations, and the best results were achieved by the Inception model with a 0.001 learning rate, whose accuracy on the test set was 73.3%. The results can be considered promising and suggest that the proposed method could be used to assist dentists in the evaluation of bitewing images, and the definition of lesion severity and appropriate treatments.<\/jats:p>","DOI":"10.3390\/s21155192","type":"journal-article","created":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T21:44:32Z","timestamp":1627854272000},"page":"5192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks"],"prefix":"10.3390","volume":"21","author":[{"given":"Maira","family":"Moran","sequence":"first","affiliation":[{"name":"Policl\u00ednica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil"},{"name":"Instituto de Computa\u00e7\u00e3o, Universidade Federal Fluminense, Niter\u00f3i 24210-310, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcelo","family":"Faria","sequence":"additional","affiliation":[{"name":"Policl\u00ednica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil"},{"name":"Faculdade de Odontologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-617, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gilson","family":"Giraldi","sequence":"additional","affiliation":[{"name":"Laborat\u00f3rio Nacional de Computa\u00e7\u00e3o Cient\u00edfica, Petr\u00f3polis 25651-076, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luciana","family":"Bastos","sequence":"additional","affiliation":[{"name":"Policl\u00ednica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Larissa","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Policl\u00ednica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0782-2501","authenticated-orcid":false,"given":"Aura","family":"Conci","sequence":"additional","affiliation":[{"name":"Instituto de Computa\u00e7\u00e3o, Universidade Federal Fluminense, Niter\u00f3i 24210-310, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/S0011-8532(22)00819-9","article-title":"Dental Caries Diagnosis","volume":"43","author":"Stookey","year":"1999","journal-title":"Dent. 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