{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:40:07Z","timestamp":1781883607247,"version":"3.54.5"},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T00:00:00Z","timestamp":1663977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Caries prevention is essential for oral hygiene. A fully automated procedure that reduces human labor and human error is needed. This paper presents a fully automated method that segments tooth regions of interest from a panoramic radiograph to diagnose caries. A patient\u2019s panoramic oral radiograph, which can be taken at any dental facility, is first segmented into several segments of individual teeth. Then, informative features are extracted from the teeth using a pre-trained deep learning network such as VGG, Resnet, or Xception. Each extracted feature is learned by a classification model such as random forest, k-nearest neighbor, or support vector machine. The prediction of each classifier model is considered as an individual opinion that contributes to the final diagnosis, which is decided by a majority voting method. The proposed method achieved an accuracy of 93.58%, a sensitivity of 93.91%, and a specificity of 93.33%, making it promising for widespread implementation. The proposed method, which outperforms existing methods in terms of reliability, and can facilitate dental diagnosis and reduce the need for tedious procedures.<\/jats:p>","DOI":"10.3390\/e24101358","type":"journal-article","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T23:13:27Z","timestamp":1664147607000},"page":"1358","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2449-5653","authenticated-orcid":false,"given":"Toan Huy","family":"Bui","sequence":"first","affiliation":[{"name":"Course of Science and Technology, Graduate School of Science and Technology, Tokai University, Tokyo 108-8619, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2454-3512","authenticated-orcid":false,"given":"Kazuhiko","family":"Hamamoto","sequence":"additional","affiliation":[{"name":"Graduate School of Information and Telecommunication Engineering, Tokai University, Tokyo 108-8619, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"May Phu","family":"Paing","sequence":"additional","affiliation":[{"name":"School of Engineering, King Mongkut\u2019s Institute of Technology Ladkrabang, Bangkok 10520, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2020, October 01). 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