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Accuracy of Panoramic Radiograph for Diagnosing Periodontitis Comparing to Clinical Examination
In this study, we explore the diagnostic accuracy of a Radiographic-based Periodontal Bone Loss (R-PBL) method as a screening tool for periodontitis, in the form of radiographic bone loss, under the 2018 case definition in comparison to the 2012 case definition. The analysis was based on 456 patient...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7408796/ https://www.ncbi.nlm.nih.gov/pubmed/32708230 http://dx.doi.org/10.3390/jcm9072313 |
Sumario: | In this study, we explore the diagnostic accuracy of a Radiographic-based Periodontal Bone Loss (R-PBL) method as a screening tool for periodontitis, in the form of radiographic bone loss, under the 2018 case definition in comparison to the 2012 case definition. The analysis was based on 456 patients (253 females and 203 males), screened for periodontal status in the Study of Periodontal Health in Almada-Seixal (SoPHiAS) project and subjected to a panoramic dental X-ray. Patients were diagnosed for the presence of periodontitis following the 2018 and 2012 case definition. R-PBL classification was defined by alveolar bone loss and diagnosed as no periodontitis (≥80% remaining alveolar bone), mild to moderate periodontitis (66% to 79%), or severe periodontitis (<66%). We appraise the X-ray quality to look for the influence on the performance of R-PBL. Sensitivity, specificity, accuracy, and precision, through several indicators, were determined. Performance measurement was assessed through binary and multiclass Receiver operating characteristic/are under the curve (ROC/AUC) analyses. Our results show that the tested R-PBL method under the 2018 case definition is a reliable tool in periodontitis cases screening. This method does not replace clinical periodontal evaluation, but rather, it screens patients towards a definitive periodontitis diagnosis. These results will contribute to support the development of automated prediction systems towards periodontitis surveillance. |
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