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An Endodontic Forecasting Model Based on the Analysis of Preoperative Dental Radiographs: A Pilot Study on an Endodontic Predictive Deep Neural Network

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dc.contributor.author김선일-
dc.contributor.author김의성-
dc.contributor.author이채나-
dc.contributor.author최윤정-
dc.contributor.author이정훈-
dc.contributor.author이석준-
dc.date.accessioned2023-08-23T00:01:39Z-
dc.date.available2023-08-23T00:01:39Z-
dc.date.issued2023-06-
dc.identifier.issn0099-2399-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/196133-
dc.description.abstractIntroduction: This study aimed to evaluate the use of deep convolutional neural network (DCNN) algorithms to detect clinical features and predict the three-year outcome of endodontic treatment on preoperative periapical radiographs. Methods: A database of single-root premolars that received endodontic treatment or retreatment by endodontists with presence of three-year outcome was prepared (n = 598). We constructed a 17-layered DCNN with a self-attention layer (Periapical Radiograph Explanatory System with Self-Attention Network [PRESSAN-17]), and the model was trained, validated, and tested to 1) detect 7 clinical features, that is, full coverage restoration, presence of proximal teeth, coronal defect, root rest, canal visibility, previous root filling, and periapical radiolucency and 2) predict the three-year endodontic prognosis by analyzing preoperative periapical radiographs as an input. During the prognostication test, a conventional DCNN without a self-attention layer (residual neural network [RESNET]-18) was tested for comparison. Accuracy and area under the receiver-operating-characteristic curve were mainly evaluated for performance comparison. Gradient-weighted class activation mapping was used to visualize weighted heatmaps. Results: PRESSAN-17 detected full coverage restoration (area under the receiver-operating-characteristic curve = 0.975), presence of proximal teeth (0.866), coronal defect (0.672), root rest (0.989), previous root filling (0.879), and periapical radiolucency (0.690) significantly, compared to the no-information rate (P < .05). Comparing the mean accuracy of 5-fold validation of 2 models, PRESSAN-17 (67.0%) showed a significant difference to RESNET-18 (63.4%, P < .05). Also, the area under average receiver-operating-characteristic of PRESSAN-17 was 0.638, which was significantly different compared to the no-information rate. Gradient-weighted class activation mapping demonstrated that PRESSAN-17 correctly identified clinical features. Conclusions: Deep convolutional neural networks can detect several clinical features in periapical radiographs accurately. Based on our findings, well-developed artificial intelligence can support clinical decisions related to endodontic treatments in dentists.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherElsevier-
dc.relation.isPartOfJOURNAL OF ENDODONTICS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHArtificial Intelligence*-
dc.subject.MESHNeural Networks, Computer-
dc.subject.MESHPilot Projects-
dc.subject.MESHRadiography-
dc.subject.MESHRoot Canal Therapy*-
dc.titleAn Endodontic Forecasting Model Based on the Analysis of Preoperative Dental Radiographs: A Pilot Study on an Endodontic Predictive Deep Neural Network-
dc.typeArticle-
dc.contributor.collegeCollege of Dentistry (치과대학)-
dc.contributor.departmentDept. of Conservative Dentistry (보존과학교실)-
dc.contributor.googleauthorJunghoon Lee-
dc.contributor.googleauthorHyunseok Seo-
dc.contributor.googleauthorYoon Jeong Choi-
dc.contributor.googleauthorChena Lee-
dc.contributor.googleauthorSunil Kim-
dc.contributor.googleauthorYe Sel Lee-
dc.contributor.googleauthorSukjoon Lee-
dc.contributor.googleauthorEuiseong Kim-
dc.identifier.doi10.1016/ j.joen.2023.03.015-
dc.contributor.localIdA00556-
dc.contributor.localIdA00833-
dc.contributor.localIdA05388-
dc.contributor.localIdA04139-
dc.relation.journalcodeJ01393-
dc.identifier.eissn1878-3554-
dc.identifier.pmid37019378-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0099239923001784-
dc.subject.keywordConvolutional neural network-
dc.subject.keywordGrad-CAM-
dc.subject.keywordPRESSAN-17-
dc.subject.keywordartificial intelligence-
dc.subject.keywordendodontic outcome-
dc.subject.keywordendodontic treatment-
dc.contributor.alternativeNameKim, Sun Il-
dc.contributor.affiliatedAuthor김선일-
dc.contributor.affiliatedAuthor김의성-
dc.contributor.affiliatedAuthor이채나-
dc.contributor.affiliatedAuthor최윤정-
dc.citation.volume49-
dc.citation.number6-
dc.citation.startPage710-
dc.citation.endPage719-
dc.identifier.bibliographicCitationJOURNAL OF ENDODONTICS, Vol.49(6) : 710-719, 2023-06-
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Conservative Dentistry (보존과학교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Radiology (영상치의학교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Orthodontics (교정과학교실) > 1. Journal Papers

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