Efficacy of denoising and enhancement filters for detection of approximal and occlusal caries on digital intraoral radiographs

Shokri, Abbas; Kasraei, Shahin; Lari, Sima; Mahmoodzadeh, Majid; Khaleghi, Amin; Musavi, Saeid; Akheshteh, Vahid

doi:10.4103/jcd.jcd_213_17

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…Shokri et al, analyzed the effect of filters on detecting proximal and occlusal caries employing intraoral images and concluded that the lowest accuracy in caries diagnosis was noted for the detection of enamel lesions on original radiographs (52%). However, this in vitro study induced caries by a demineralizing solution, and therefore induced carious lesions were more regular than those that developed naturally [32]. Belém et al [33] analyzed the accuracy of detection of subsurface demineralization by different imaging modalities and concluded that original images had an accuracy of 73% and a sensitivity of 62%.…”

Section: Discussionmentioning

confidence: 86%

Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review

Prados-Privado

Villalón²,

Martínez-Martínez

et al. 2020

JCM

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Dental caries is the most prevalent dental disease worldwide, and neural networks and artificial intelligence are increasingly being used in the field of dentistry. This systematic review aims to identify the state of the art of neural networks in caries detection and diagnosis. A search was conducted in PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Data extraction was performed independently by two reviewers. The quality of the selected studies was assessed using the Cochrane Handbook tool. Thirteen studies were included. Most of the included studies employed periapical, near-infrared light transillumination, and bitewing radiography. The image databases ranged from 87 to 3000 images, with a mean of 669 images. Seven of the included studies labeled the dental caries in each image by experienced dentists. Not all of the studies detailed how caries was defined, and not all detailed the type of carious lesion detected. Each study included in this review used a different neural network and different outcome metrics. All this variability complicates the conclusions that can be made about the reliability or not of a neural network to detect and diagnose caries. A comparison between neural network and dentist results is also necessary.

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Section: Discussionmentioning

confidence: 86%

Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review

Prados-Privado

Villalón²,

Martínez-Martínez

et al. 2020

JCM

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“…In Shokri et al 19 study, the sensitivity and accuracy of filtered images were significantly higher than original images; this difference was higher in superficial images. Caries in this study was artificially created chemically and had more regular outlines; this affects caries detection on radiographs.…”

Section: Discussionmentioning

confidence: 83%

In vitro comparison of two photostimulable phosphor plate systems for early detection of occlusal dentin caries with and without a sharpening filter

Bardal

Mobini

Mirzaee

2020

J Dent Res Dent Clin Dent Prospects

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Background. Dental caries is the most important reason for tooth loss. Clinical examination is the most commonly used technique for occlusal caries diagnosis. The diagnostic power of digital systems is a matter of controversy in this field. The present study aimed to determine the diagnostic accuracy of two photostimulable phosphor plate (PSP) systems for early occlusal dentin caries in vitro. Methods. Sixty-nine extracted molar and premolar teeth were used in this study. The teeth were mounted in triple blocks, and standard radiographs were taken by the Digora and Acteon digital radiographic systems. The original and filter 1-enhanced radiographs were evaluated by two experienced observers twice at an interval of two weeks, and dentin caries was recorded in Tables prepared for the study. The teeth were then sectioned in a buccolingual direction and evaluated under a stereomicroscope. The observers’ reports were compared with microscopic findings as the gold standard. SPSS 23 was used to calculate the kappa coefficient, sensitivity, specificity, and area under the ROC curve (AUC). Statistical significance was set at P<0.05. Results. The internal and the external agreements in both imaging systems were good to excellent. The means of sensitivity, specificity, and AUC in the Acteon system were 34.1, 92.9, and 0.674, with 30.8, 94.8, and 0.659, respectively, in the Digora system. Conclusion. The accuracy of early occlusal caries diagnosis was poor on both systems, and no significant difference was observed between the two systems at a 95% confidence interval. Although the AUC was slightly higher in the original images, there was no significant difference between them; however, due to their high specificity, they can prevent unnecessary treatments in the clinic.

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Evaluation of the effects of postprocessing settings in digital bitewing radiographs on proximal caries detection

Abdinian,

Keshani,

Sadeghi

et al. 2024

Clinical & Exp Dental Res

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ObjectiveRadiographs are an integral part of detecting proximal caries. The aim of this study was to evaluate the effect of contrast, brightness, noise, sharpness, and γ adjustment of digital intraoral radiographs on the diagnosis of proximal caries.Materials and methodsIn this in vitro study, 40 extracted teeth including 20 premolars and 20 molars with enamel lesions (white spot or dentin discoloration seen through the enamel) were mounted together in groups of eight inside the skull. Bitewing radiographic images of each dental group were obtained by a photostimulable phosphor plate sensor with exposure conditions of 8 mA, 70 kV, and 0.2 s. The images were reconstructed by the built‐in software and examined by two oral and maxillofacial radiologists in various settings of contrast, brightness, sharpness, noise, and γ. The teeth were then cut mesiodistally and the presence or absence of caries was confirmed by an oral and maxillofacial pathologist using a stereomicroscope. The data were then analyzed using the κ agreement coefficient, sensitivity, specificity, and accuracy (α = .05).ResultsAdjustment of brightness and contrast led to higher diagnostic performance with an accuracy of 82.5% and 83.8 (for observers 1 and 2, respectively) and 82.5% (for both observers), respectively. Noise adjustment was the least helpful approach for diagnosis of proximal dental caries among other adjustments, with an accuracy of 78.8% and 77.5% for observers 1 and 2, respectively.ConclusionBrightness and contrast setting was more efficient in improving the diagnostic potential of bitewing radiographs compared to other adjustments.

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Efficacy of denoising and enhancement filters for detection of approximal and occlusal caries on digital intraoral radiographs

Cited by 7 publications

References 19 publications

Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review

Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review

In vitro comparison of two photostimulable phosphor plate systems for early detection of occlusal dentin caries with and without a sharpening filter

Evaluation of the effects of postprocessing settings in digital bitewing radiographs on proximal caries detection

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