Assessment of Intra- and Inter-examiner Reproducibility of Probing Depth Measurements with a Manual Periodontal Probe

Lafzi, Ardeshir; Mohammadi, Adileh Shir; Eskandari, Ali; Pourkhamneh, Sohrab

doi:10.5681/joddd.2007.004

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…To study periodontal disease progression and treatment outcomes in a large cohort, a retrospective review of periodontal charting is frequently performed. However, inconsistency of periodontal charting between different care providers may affect data reliability (Buduneli et al, 2004;Lafzi et al, 2007). Bone level changes in periapical images reviewed by the proposed DL model can be used to validate the results of periodontal charting (Machtei et al, 1997;Zaki et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Use of the deep learning approach to measure alveolar bone level

Lee¹,

Kabir²,

Nelson³

et al. 2021

J Clinic Periodontology

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Aim: The goal was to use a deep convolutional neural network to measure the radiographic alveolar bone level to aid periodontal diagnosis. Materials and Methods: A deep learning (DL) model was developed by integrating three segmentation networks (bone area, tooth, cemento-enamel junction) and image analysis to measure the radiographic bone level and assign radiographic bone loss (RBL) stages. The percentage of RBL was calculated to determine the stage of RBL for each tooth. A provisional periodontal diagnosis was assigned using the 2018 periodontitis classification. RBL percentage, staging, and presumptive diagnosis were compared with the measurements and diagnoses made by the independent examiners. Results:The average Dice Similarity Coefficient (DSC) for segmentation was over 0.91. There was no significant difference in the RBL percentage measurements determined by DL and examiners (p ¼ :65). The area under the receiver operating characteristics curve of RBL stage assignment for stages I, II, and III was 0.89, 0.90, and 0.90, respectively. The accuracy of the case diagnosis was 0.85. Conclusions:The proposed DL model provides reliable RBL measurements and image-based periodontal diagnosis using periapical radiographic images. However, this model has to be further optimized and validated by a larger number of images to facilitate its application.

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Section: Discussionmentioning

confidence: 99%

Use of the deep learning approach to measure alveolar bone level

Lee¹,

Kabir²,

Nelson³

et al. 2021

J Clinic Periodontology

View full text Add to dashboard Cite

show abstract

“…Despite many advances in dental technology, the periodontal probe remains the most frequently used diagnostic tool for assessing the health status and attachment level of periodontal tissues 1 . It is well known that the accuracy and reproducibility of the clinical pocket probing depth (PD) can be affected by the probing force, presence of periodontitis, type of periodontal probe, and probing site 2 – 4 . The resulting errors can impact clinical decision-making, especially during longitudinal monitoring of the periodontal status 5 .…”

Section: Introductionmentioning

confidence: 99%

Periodontal probing on digital images compared to clinical measurements in periodontitis patients

Chung

Park

et al. 2022

Sci Rep

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The aim of the study was to compare the supra-alveolar gingival dimension (GD) and the clinical pocket probing depth (PD) by combining data from an intraoral scanner (IOS) and cone-beam computed tomography (CBCT) and identify the clinical features affecting the clinical PD. 1,071 sites from 11 patients were selected for whom CBCT, IOS images, and periodontal charts were recorded at the same visit. CBCT and IOS data were superimposed. GD was measured on cross-sectional images of the probed sites. The level of agreement and correlation between GD and PD were assessed for the entire population and within groups (treated vs untreated, bleeding on probing [BOP] vs no BOP, and PDs of 0–3 mm vs 4–5 mm vs ≥ 6 mm). The mean [± SD] difference between GD and PD was 0.82 [± 0.69] mm, and they were positively correlated (r = 0.790, p < 0.001). The correlations between GD and PD were stronger for untreated sites, sites with BOP, and sites with a larger PD. Within the limitations of this study, the similarity between GD and PD may suggest a possible tendency of overestimation when recording PD.

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Feasibility of training community health workers to conduct periodontal examinations: a validation study in rural Nepal

Erchick

Agrawal

Khatry³

et al. 2020

BMC Health Serv Res

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Background: In many low-and middle-income countries, insufficient human resources limit access to oral health services. Shifting clinical tasks to less specialized health professionals, such as community health workers, has been used as a strategy to expand the health workforce, especially in remote or underserved locations. The objective of this study was to evaluate the validity of periodontal examinations conducted by auxiliary nurse midwives in a rural home setting in Nepal. Methods: Twenty-one pregnant women < 26 weeks gestation from Sarlahi District, Nepal, underwent full mouth periodontal examinations measuring probing depth (PD) and bleeding on probing (BOP) on 6 sites per tooth by one of five auxiliary nurse midwives, who were trained for this study but had no previous training in dentistry. After a 15-min break, each participant was examined again by an experienced dentist. Measures of validity for PD and BOP were calculated comparing the pooled and individual auxiliary nurse midwives to the dentist. A multivariable GEE model estimated the effect of periodontal characteristics on agreement between the auxiliary nurse midwives and the dentist. Results: Participant mean age was 22 years (SD: ±3 years), mean PD was 1.4 mm (SD: 03 mm), and 86% of women had BOP (according to the dentist). Percent agreement, weighted kappa scores, and intraclass correlation coefficients for PD, with an allowance of ±1 mm, exceeded 99%, 0.7, and 0.9, respectively, indicating an acceptable level of agreement. Auxiliary nurse midwives tended to report higher PD scores relative to the dentist, although this over-estimation was small and unlikely to impact population-based estimates of important indicators of oral health status. GEE regression modeling indicated similar agreement for mandible vs. maxilla, left vs. right side, and PD (≤2 mm, > 2 mm), and lower agreement for posterior teeth and lingual and proximal sites.

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Assessment of Intra- and Inter-examiner Reproducibility of Probing Depth Measurements with a Manual Periodontal Probe

Cited by 4 publications

References 15 publications

Use of the deep learning approach to measure alveolar bone level

Use of the deep learning approach to measure alveolar bone level

Periodontal probing on digital images compared to clinical measurements in periodontitis patients

Feasibility of training community health workers to conduct periodontal examinations: a validation study in rural Nepal

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