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Deep learning for early dental caries detection in bitewing radiographs

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dc.contributor.author강수미-
dc.contributor.author박정원-
dc.contributor.author신유석-
dc.contributor.author강수미-
dc.contributor.author박정원-
dc.contributor.author신유석-
dc.date.accessioned2021-12-28T17:02:45Z-
dc.date.available2021-12-28T17:02:45Z-
dc.date.issued2021-08-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/186909-
dc.description.abstractThe early detection of initial dental caries enables preventive treatment, and bitewing radiography is a good diagnostic tool for posterior initial caries. In medical imaging, the utilization of deep learning with convolutional neural networks (CNNs) to process various types of images has been actively researched, with promising performance. In this study, we developed a CNN model using a U-shaped deep CNN (U-Net) for caries detection on bitewing radiographs and investigated whether this model can improve clinicians' performance. The research complied with relevant ethical regulations. In total, 304 bitewing radiographs were used to train the CNN model and 50 radiographs for performance evaluation. The diagnostic performance of the CNN model on the total test dataset was as follows: precision, 63.29%; recall, 65.02%; and F1-score, 64.14%, showing quite accurate performance. When three dentists detected caries using the results of the CNN model as reference data, the overall diagnostic performance of all three clinicians significantly improved, as shown by an increased sensitivity ratio (D1, 85.34%; D1', 92.15%; D2, 85.86%; D2', 93.72%; D3, 69.11%; D3', 79.06%; p < 0.05). These increases were especially significant (p < 0.05) in the initial and moderate caries subgroups. The deep learning model may help clinicians to diagnose dental caries more accurately.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.relation.isPartOfSCIENTIFIC REPORTS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHDeep Learning*-
dc.subject.MESHDental Caries / diagnosis*-
dc.subject.MESHDental Caries / diagnostic imaging*-
dc.subject.MESHHumans-
dc.subject.MESHNeural Networks, Computer-
dc.subject.MESHRadiography, Bitewing*-
dc.titleDeep learning for early dental caries detection in bitewing radiographs-
dc.typeArticle-
dc.contributor.collegeCollege of Dentistry (치과대학)-
dc.contributor.departmentDept. of Conservative Dentistry (보존과학교실)-
dc.contributor.googleauthorShinae Lee-
dc.contributor.googleauthorSang-Il Oh-
dc.contributor.googleauthorJunik Jo-
dc.contributor.googleauthorSumi Kang-
dc.contributor.googleauthorYooseok Shin-
dc.contributor.googleauthorJeong-Won Park-
dc.identifier.doi10.1038/s41598-021-96368-7-
dc.contributor.localIdA04872-
dc.contributor.localIdA01649-
dc.contributor.localIdA02129-
dc.contributor.localIdA04872-
dc.contributor.localIdA01649-
dc.contributor.localIdA02129-
dc.relation.journalcodeJ02646-
dc.identifier.eissn2045-2322-
dc.identifier.pmid34413414-
dc.contributor.alternativeNameKang, Sumi-
dc.contributor.affiliatedAuthor강수미-
dc.contributor.affiliatedAuthor박정원-
dc.contributor.affiliatedAuthor신유석-
dc.contributor.affiliatedAuthor강수미-
dc.contributor.affiliatedAuthor박정원-
dc.contributor.affiliatedAuthor신유석-
dc.citation.volume11-
dc.citation.number1-
dc.citation.startPage16807-
dc.identifier.bibliographicCitationSCIENTIFIC REPORTS, Vol.11(1) : 16807, 2021-08-
Appears in Collections:
2. College of Dentistry (치과대학) > Dental Education Research Center (치의학교육교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Conservative Dentistry (보존과학교실) > 1. Journal Papers

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