Validation of a dental image‐analyzer tool to measure the radiographic defect angle of the intrabony defect in periodontitis patients

Moutinho, R. P.; Coelho, Luís; Silva, Augusto; Pereira, José António; Pinto, Miguel; Baptista, Isabel

doi:10.1111/j.1600-0765.2012.01483.x

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…Automatic detection of the CEJ has been carried out by previous research in IOPAs [5,15]. In the present study, we have tried to provide a tool for screening patients' OPGs showing bone loss.…”

Section: Discussionmentioning

confidence: 99%

“…Published studies have shown various techniques for estimating the interdental area and the localization of the CEJ [5]. They have estimated the area as determined by a dentist (utilized as ground truth) and compared it to the software estimation.…”

Section: Discussionmentioning

confidence: 99%

“…The earlier method used for measuring the degree of bone loss was conducted via a Schei ruler [4,5]. The digital revolution could be traced with the development of the DIA tool in 2009 [3], automated CEJ localization and bone loss area assessment [4,6], Multimodal 3D imaging [7], development of a deep CNN algorithm in 2018 and 2019 [8,9], and the deep learning hybrid method [10].…”

Section: Introductionmentioning

confidence: 99%

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Development of a Computational Tool for the Estimation of Alveolar Bone Loss in Oral Radiographic Images

et al. 2022

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The present study evaluated a newly developed computational tool (CT) to assess the alveolar bone space and the alveolar crest angle and compares it to dentist assessment (GT). The novel tool consisted of a set of processes initiated with image enhancement, points localization, and angle and area calculations. In total, we analyzed 148 sites in 39 radiographic images, and among these, 42 sites were selected and divided into two groups of non-periodontitis and periodontitis. The alveolar space area (ASA) and alveolar crest angle (ACA) were estimated. The agreement between the computer software and the ground truth was analyzed using the Bland–Altman plot. The sensitivity and specificity of the computer tool were measured using the ROC curve. The Bland–Altman plot showed an agreement between the ground truth and the computational tool in all of the parameters assessed. The ROC curve showed 100% sensitivity and 100% specificity for 12.67 mm of the alveolar space area. The maximum percentage of sensitivity and specificity were 80.95% for 13.63 degrees of the alveolar crest angle. Computer tool assessment provides accurate disease severity and treatment monitoring for evaluating the alveolar space area (ASA) and the alveolar crest angle (ACA).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of a Computational Tool for the Estimation of Alveolar Bone Loss in Oral Radiographic Images

et al. 2022

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“…Computer-assisted systems have been actively studied to improve the accuracy of dental health screening. In particular, radiographic and photographic intra-oral images have been intensively discussed when studying alveolar bone loss and periodontal disease [ 19 , 20 , 21 ]. Image analysis for periodontal diagnosis has mainly interpreted correlation with the disease by extracting features of abnormal symptoms, such as the degree of alveolar bone loss and periodontal redness.…”

Section: Introductionmentioning

confidence: 99%

Intra-Oral Photograph Analysis for Gingivitis Screening in Orthodontic Patients

Kim

Lee

2023

IJERPH

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This study aimed to confirm the presence of gingival inflammation through image analysis of the papillary gingiva using intra-oral photographs (IOPs) before and after orthodontic treatment and to confirm the possibility of using gingival image analysis for gingivitis screening. Five hundred and eighty-eight (n = 588) gingival sites from the IOPs of 98 patients were included. Twenty-five participants who had completed their orthodontic treatments and were aged between 20 and 37 were included. Six points on the papillary gingiva were selected in the maxillary and mandibular anterior incisors. The red/green (R/G) ratio values were obtained for the selected gingival images and the modified gingival index (GI) was compared. The change in the R/G values during the orthodontic treatment period appeared in the order of before orthodontic treatment (BO), mid-point of orthodontic treatment (MO), three-quarters of the way through orthodontic treatment (TO), and immediately after debonding (IDO), confirming that it was similar to the change in the GI. The R/G value of the gingiva in the image correlated with the GI. Therefore, it could be used as a major index for gingivitis diagnosis using images.

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“…Recently, a report on validation of a dental image analyzer tool to measure alveolar bone loss are available in periodontitis research literature [2], in which bone-loss areas are manually drawn by dentists. For automatic measurement of alveolar bone loss, suspicious bone-loss areas must firstly be identified and localized.…”

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confidence: 99%

Alveolar bone-loss area detection in periodontitis radiographs using hybrid of intensity and texture analyzed based on FBM model

Huang

Lin

et al. 2014

2014 International Conference on Machine Learning and Cybernetics

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Automatic detection of alveolar bone-loss areas in dental periapical radiographs is a very challenging task because of the common uneven illumination problem of dental radiographs and complex topology of bone-loss areas. In this paper, we propose an effective automatic detection method ABL-Imm, which uses weighted average of both the intensity and the texture measured by the H-value of fractal Brownian motions (mm) model. The weights are trained with receiver operating characteristics (ROC) curve based on leave-one-out cross validation mechanism and the principle of the minimum area under the ROC curve (AUC). Through the weighted average of both features, radiograph images are transformed into feature images with the histogram near bimodal distribution. Finally, feature images are segmented into normal and bone-loss regionsby Otsu's auto-thresholding. We test on eight periodontitis radiograph images using the proposed ABL-Imm, the methods with only the feature of mm-H or the intensity, and a method based on level set segmentation, respectively. Experimental results showed that among all the test methods, our proposed ABL-Imm has the highest average TPVF and the lowest average FPVF, when compared with the ground truth (GT) provided by dentists.

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Validation of a dental image‐analyzer tool to measure the radiographic defect angle of the intrabony defect in periodontitis patients

Abstract: This study provided evidence for the lack of a significant difference between the conventional method and the DIA tool for radiographic measurement of intrabony defects. However, digital analysis was significantly faster.

Cited by 14 publications

References 18 publications

Development of a Computational Tool for the Estimation of Alveolar Bone Loss in Oral Radiographic Images

Development of a Computational Tool for the Estimation of Alveolar Bone Loss in Oral Radiographic Images

Intra-Oral Photograph Analysis for Gingivitis Screening in Orthodontic Patients

Alveolar bone-loss area detection in periodontitis radiographs using hybrid of intensity and texture analyzed based on FBM model

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