Application of Information Technologies and Quantitative Light-Induced Fluorescence for the Assessment of Early Caries Treatment Outcomes

Маслак, Е. Е.; Khudanov, Bakhtinur; Krivtsova, Darya; Tsoy, Tatyana

doi:10.1109/dese.2019.00169

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…Several studies [ 8 , 9 , 10 ] have advocated the use of photoacoustic images, wavelength images, or ultrasound imaging for caries detection. Other studies [ 10 , 11 ] have proposed an approach that employs an RGB oral endoscope image. However, most systems cannot observe the detailed anatomy of a tooth, especially the root, and hence cannot be used to diagnose caries.…”

Section: Introductionmentioning

confidence: 99%

Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

Bui

Hamamoto

Paing

2022

Entropy

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Caries prevention is essential for oral hygiene. A fully automated procedure that reduces human labor and human error is needed. This paper presents a fully automated method that segments tooth regions of interest from a panoramic radiograph to diagnose caries. A patient’s panoramic oral radiograph, which can be taken at any dental facility, is first segmented into several segments of individual teeth. Then, informative features are extracted from the teeth using a pre-trained deep learning network such as VGG, Resnet, or Xception. Each extracted feature is learned by a classification model such as random forest, k-nearest neighbor, or support vector machine. The prediction of each classifier model is considered as an individual opinion that contributes to the final diagnosis, which is decided by a majority voting method. The proposed method achieved an accuracy of 93.58%, a sensitivity of 93.91%, and a specificity of 93.33%, making it promising for widespread implementation. The proposed method, which outperforms existing methods in terms of reliability, and can facilitate dental diagnosis and reduce the need for tedious procedures.

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Section: Introductionmentioning

confidence: 99%

Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

Bui

Hamamoto

Paing

2022

Entropy

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“…Some researchers proposed detection using photoacoustic images, wavelengths, or ultrasound images [5][6][7]. Other research has detailed an approach using an RGB oral endoscope image [8,9]; however, most approaches cannot provide a detailed structure of the tooth, especially the tooth root, and therefore struggle to provide caries diagnosis. Compared to oral endoscope imaging, dental radiographs provide greater image quality and detect detailed structure deformity in the tooth [10]; therefore, the dental radiographic image is the most well-known approach, and is preferable for the detection of caries in the early stage.…”

Section: Introductionmentioning

confidence: 99%

Deep Fusion Feature Extraction for Caries Detection on Dental Panoramic Radiographs

2021

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Caries is the most well-known disease and relates to the oral health of billions of people around the world. Despite the importance and necessity of a well-designed detection method, studies in caries detection are still limited and show a restriction in performance. In this paper, we proposed a computer-aided diagnosis (CAD) method to detect caries among normal patients using dental radiographs. The proposed method mainly consists of two processes: feature extraction and classification. In the feature extraction phase, the chosen 2D tooth image was employed to extract deep activated features using a deep pre-trained model and geometric features using mathematic formulas. Both feature sets were then combined, called fusion feature, to complement each other defects. Then, the optimal fusion feature set was fed into well-known classification models such as support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), Naïve Bayes (NB), and random forest (RF) to determine the best classification model that fit the fusion features set and perform the most preeminent result. The results show 91.70%, 90.43%, and 92.67% for accuracy, sensitivity, and specificity, respectively. The proposed method has outperformed the previous state-of-the-art and shows promising results when none of the measured factors is less than 90%; therefore, the method is promising for dentists and capable of wide-scale implementation caries detection in hospitals.

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Application of Information Technologies and Quantitative Light-Induced Fluorescence for the Assessment of Early Caries Treatment Outcomes

Cited by 2 publications

References 28 publications

Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

Deep Fusion Feature Extraction for Caries Detection on Dental Panoramic Radiographs

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