Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study

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dc.contributor.authorKurt-Bayrakdar, Sevda
dc.contributor.authorBayrakdar, İbrahim Şevki
dc.contributor.authorYavuz, Muhammet Burak
dc.contributor.authorSalih, Nichal
dc.contributor.authorÇelik, Özer
dc.contributor.authorKöse, Oğuz
dc.contributor.authorUzun Saylan, Bilge Cansu
dc.contributor.authorKuleli, Batuhan
dc.contributor.authorJogtap, Rohan
dc.contributor.authorOrhan, Kaan
dc.date.accessioned2025-01-12T17:20:06Z
dc.date.available2025-01-12T17:20:06Z
dc.date.issued2024
dc.departmentKaramanoğlu Mehmetbey Üniversitesi
dc.description.abstractBackgroundThis retrospective study aimed to develop a deep learning algorithm for the interpretation of panoramic radiographs and to examine the performance of this algorithm in the detection of periodontal bone losses and bone loss patterns.MethodsA total of 1121 panoramic radiographs were used in this study. Bone losses in the maxilla and mandibula (total alveolar bone loss) (n = 2251), interdental bone losses (n = 25303), and furcation defects (n = 2815) were labeled using the segmentation method. In addition, interdental bone losses were divided into horizontal (n = 21839) and vertical (n = 3464) bone losses according to the defect patterns. A Convolutional Neural Network (CNN)-based artificial intelligence (AI) system was developed using U-Net architecture. The performance of the deep learning algorithm was statistically evaluated by the confusion matrix and ROC curve analysis.ResultsThe system showed the highest diagnostic performance in the detection of total alveolar bone losses (AUC = 0.951) and the lowest in the detection of vertical bone losses (AUC = 0.733). The sensitivity, precision, F1 score, accuracy, and AUC values were found as 1, 0.995, 0.997, 0.994, 0.951 for total alveolar bone loss; found as 0.947, 0.939, 0.943, 0.892, 0.910 for horizontal bone losses; found as 0.558, 0.846, 0.673, 0.506, 0.733 for vertical bone losses and found as 0.892, 0.933, 0.912, 0.837, 0.868 for furcation defects (respectively).ConclusionsAI systems offer promising results in determining periodontal bone loss patterns and furcation defects from dental radiographs. This suggests that CNN algorithms can also be used to provide more detailed information such as automatic determination of periodontal disease severity and treatment planning in various dental radiographs.
dc.description.sponsorshipEskisehir Osmangazi University Scientific Research Projects Coordination Unit [202045E06]
dc.description.sponsorshipThis study has been supported by Eskisehir Osmangazi University Scientific Research Projects Coordination Unit under project number 202045E06.
dc.identifier.doi10.1186/s12903-024-03896-5
dc.identifier.issn1472-6831
dc.identifier.issue1
dc.identifier.pmid38297288
dc.identifier.urihttps://doi.org/10.1186/s12903-024-03896-5
dc.identifier.volume24
dc.identifier.wosWOS:001155628800004
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherBmc
dc.relation.ispartofBmc Oral Health
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20250111
dc.subjectPanoramic radiography
dc.subjectArtificial intelligence
dc.subjectPeriodontitis
dc.subjectDentistry
dc.titleDetection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study
dc.typeArticle

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