Computer–Automated Caries Detection in  Digital Bitewings: Consistency of a Program and Its Influence on Observer Agreement

Wenzel, Ann

doi:10.1159/000047425

Cited by 32 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, Logicon appears to be more reliable in ruling out carious lesions than in detecting them whether in the enamel or in dentin ( 17 ). In another study, Wenzel et al ( 18 ) found mean kappa value for inter-observer agreement for caries scores to be 0.47 before and 0.48 after the use of LCD, which did not improve using the program. They also found that the program was not consistent and provided diverse opinions on the caries status.…”

Section: Discussionmentioning

confidence: 95%

Designing of a Computer Software for Detection of Approximal Caries in Posterior Teeth

et al. 2015

View full text Add to dashboard Cite

Background:Radiographs, adjunct to clinical examination are always valuable complementary methods for dental caries detection. Recently, progressing in digital imaging system provides possibility of software designing for automatically dental caries detection.Objectives:The aim of this study was to develop and assess the function of diagnostic computer software designed for evaluation of approximal caries in posterior teeth. This software should be able to indicate the depth and location of caries on digital radiographic images.Materials and Methods:Digital radiographs were obtained of 93 teeth including 183 proximal surfaces. These images were used as a database for designing the software and training the software designer. In the design phase, considering the summed density of pixels in rows and columns of the images, the teeth were separated from each other and the unnecessary regions; for example, the root area in the alveolar bone was eliminated. Therefore, based on summed intensities, each image was segmented such that each segment contained only one tooth. Subsequently, based on the fuzzy logic, a well-known data-clustering algorithm named fuzzy c-means (FCM) was applied to the images to cluster or segment each tooth. This algorithm is referred to as a soft clustering method, which assigns data elements to one or more clusters with a specific membership function. Using the extracted clusters, the tooth border was determined and assessed for cavity. The results of histological analysis were used as the gold standard for comparison with the results obtained from the software. Depth of caries was measured, and finally Intraclass Correlation Coefficient (ICC) and Bland-Altman plot were used to show the agreement between the methods.Results:The software diagnosed 60% of enamel caries. The ICC (for detection of enamel caries) between the computer software and histological analysis results was determined as 0.609 (95% confidence interval [CI] = 0.159-0.849) (P = 0.006). Also, the computer program diagnosed 97% of dentin caries and the ICC between the software and histological analysis results for dentin caries was determined as 0.937 (95% CI=0.906-0.958) (P < 0.001). Bland-Altman plot showed an acceptable agreement for measuring the depth of caries in enamel and dentin.Conclusions:The designed software was able to detect a significant number of dentin caries and acceptable measuring of the depth of carious lesions in enamel and dentin. However, the software had limited ability in detecting enamel lesions.

show abstract

Section: Discussionmentioning

confidence: 95%

Designing of a Computer Software for Detection of Approximal Caries in Posterior Teeth

et al. 2015

View full text Add to dashboard Cite

show abstract

“…One clinical study assessed the reproducibility of detecting approximal lesions by repeating the automated analysis 10 times for each surface, and concluded that the LCD program was not consistent and provided different outcomes for the same surface in the same image. 215 Also, observer agreement did not improve using the program. 215 In an ex vivo study, specificities for the outcome of the LCD program were significantly lower than when the observers themselves assessed the RVG images.…”

Section: Machine-intelligence Supported Systemsmentioning

confidence: 89%

“…215 Also, observer agreement did not improve using the program. 215 In an ex vivo study, specificities for the outcome of the LCD program were significantly lower than when the observers themselves assessed the RVG images. 216 Sensitivity was also lower for two observers on the diagnostic threshold caries in dentine.…”

Section: Machine-intelligence Supported Systemsmentioning

confidence: 89%

Radiographic modalities for diagnosis of caries in a historical perspective: from film to machine-intelligence supported systems

Wenzel

2021

Dentomaxillofacial Radiology

View full text Add to dashboard Cite

Radiographic imaging for the diagnosis of caries lesions has been a supplement to clinical examination for approximately a century. Various methods, and particularly X-ray receptors, have been developed over the years, and computer systems have focused on aiding the dentist in the detection of lesions and in estimating lesion depth. The present historical review has sampled accuracy ex vivo studies and clinical studies on radiographic caries diagnosis that have compared two or more receptors for capturing the image. The epochs of film radiography, xeroradiography, digital intraoral radiography, panoramic radiography and other extraoral methods, TACT analysis, cone-beam CT and artificial intelligence systems aiding in decision-making are reviewed. The author of this review (43 years in academia) has been involved in caries research and contributed to the literature in all the mentioned epochs.

show abstract

“…The mean specificity is 0.94 and 0.95, respectively. 1 Reported rater agreement varies among radiographic studies of caries detection; [2][3][4][5] this may be owing to differences in underlying sample characteristics, such as lesion depth, dentition, surface location and caries prevalence, and to methodological heterogeneity, such as the number of surfaces, number of raters and scoring categories.…”

Section: Introductionmentioning

confidence: 99%

A novel classification system for assessment of approximal caries lesion progression in bitewing radiographs

Senneby¹,

Elfvin²,

Stebring-Franzon³

et al. 2016

Dentomaxillofacial Radiology

View full text Add to dashboard Cite

Objectives: To design and pilot a novel classification system for the assessment of caries lesion progression in bitewing radiography and to report rater agreement of the system. Methods: A classification system with drawings and text was designed to assess caries lesion progression. Guidelines for Reporting Reliability and Agreement Studies were used to study and report rater agreement. Pairs of posterior bitewing radiographs (baseline and 1-year follow-up) with different status concerning caries lesion progression were selected from files from public dental health clinics. 10 raters, 5 general dental practitioners and 5 specialists in oral and maxillofacial radiology were asked to assess the radiographs with the aid of the classification system. Seven raters repeated their assessments. Rater agreement was expressed as percentage of agreement and kappa. Results: Kappa for the interrater agreement of 10 raters assessing progression was 0.61, indicating substantial agreement. Agreement was moderate for progression in the outer half of the dentine (kappa 0.55) and within enamel (kappa 0.44). Pairwise interrater agreement varied (range 69-92%; kappa 0.42-0.84). For about half of the pairs of raters, kappa was substantial ($0.61). Intrarater agreement assessing progression was substantial (kappa 0.66-0.82). Conclusions: We demonstrated the applicability of the proposed classification system on caries lesion progression with respect to rater agreement. This system can provide a common framework for clinical decision-making on caries interventional methods and patient visiting intervals. Scientifically, this system allows for a comparative analysis of different methods of prevention and treatment of caries as well as of different caries risk assessment methods. Dentomaxillofacial Radiology (2016Radiology ( ) 45, 20160039. doi: 10.1259 Cite this article as: Senneby A, Elfvin M, Stebring-Franzon C, Rohlin M. A novel classification system for assessment of approximal caries lesion progression in bitewing radiographs. Dentomaxillofac Radiol 2016; 45: 20160039.

show abstract

Computer–Automated Caries Detection in Digital Bitewings: Consistency of a Program and Its Influence on Observer Agreement

Cited by 32 publications

References 9 publications

Designing of a Computer Software for Detection of Approximal Caries in Posterior Teeth

Designing of a Computer Software for Detection of Approximal Caries in Posterior Teeth

Radiographic modalities for diagnosis of caries in a historical perspective: from film to machine-intelligence supported systems

A novel classification system for assessment of approximal caries lesion progression in bitewing radiographs

Contact Info

Product

Resources

About