Detecting Periodontal Disease Using Convolutional Neural Networks

Aberin, Shannah Therese A.; Goma, Joel C. De

doi:10.1109/hnicem.2018.8666389

Cited by 20 publications

(9 citation statements)

References 20 publications

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“…12,24,29 However, few studies have used CNN systems for evaluating the alveolar bone status in clinical dentistry, and literature reviews have noted the importance of this issue. [13][14][15][16][17]30 Aberin and Goma 13 used a CNN system for determining periodontal diseases from dental plaque microscopy images. Specifically, they used the CNN system to analyze images of periodontally healthy and unhealthy patients to match them to periodontally healthy and unhealthy conditions and achieved an accuracy of 75.5%.…”

Section: Discussionmentioning

confidence: 99%

“…[9][10][11][12] Dental radiographs are a diagnostic tool that can be effectively used to evaluate the condition of periodontal hard tissues and to analyze the success of periodontal treatment. 2,5 However, only a few studies have used CNN systems to determine periodontal disease 13,14 and alveolar bone loss [15][16][17] from radiography images. Applying a CNN system to periodontal radiography images as a decision-support mechanism for oral physicians in diagnosis and treatment planning seems promising.…”

Section: Introductionmentioning

confidence: 99%

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Success of Artificial Intelligence System in Determining Alveolar Bone Loss From Dental Panoramic Radiography Images

Kurt

Çelik

Bayrakdar³

et al. 2020

Cumhuriyet Dental Journal

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This study aims to detect alveolar bone loss from dental panoramic radiography images by using an artificial intelligence (AI) system. Materials and Methods:A total of 2276 panoramic radiography images were evaluated. Of these, 1137 were of bone loss cases and 1139 were of periodontally healthy cases. This dataset is divided into training (n = 1856), validation (n = 210), and testing (n = 210) sets. All images were resized to 1472x718 pixels before training. A random sequence was created using the open-source Python programming language and OpenCV, NumPy, Pandas, and Matplotlib libraries. A pretrained Google Net Inception v3 convolutional neural network (CNN) was used for preprocessing, and the datasets were trained using transfer learning. The diagnostic performance was evaluated using a confusion matrix in terms of the sensitivity, specificity, precision, accuracy, and F1 score.Results: Of 105 cases with bone loss, the CNN system detected 99 with sensitivity, specificity, precision, accuracy, and F1 score of 0.94, 0.88, 0.89, 0.91, and 0.91, respectively. Conclusions:The CNN system successfully determines periodontal bone loss. Therefore, it can be used to facilitate diagnosis and treatment planning by oral physicians in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Success of Artificial Intelligence System in Determining Alveolar Bone Loss From Dental Panoramic Radiography Images

Kurt

Çelik

Bayrakdar³

et al. 2020

Cumhuriyet Dental Journal

View full text Add to dashboard Cite

show abstract

“…An accuracy of 75.5% was achieved on its overall performance, in correctly differentiating images of healthy and unhealthy teeth . [49] Machine learning techniques using different data types for detecting periodontal disease have been extensively explored in literature. [50] Using SVMs (Support Vector Machines) and clinical variables, an accuracy of 88.7% was achieved in a 10-fold cross-validation.…”

Section: Prosthodonticsmentioning

confidence: 99%

Artificial intelligence in modern dentistry

Lamba

Singh

Grover

et al. 2022

ijhs

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Advancements in the domain of computer science have made artificial intelligence (AI) almost ubiquitous in everyday life. Dentistry is known to easily adapt to new technologies and hence can easily acclimatize to this nascent field. With AI spreading into dentistry, models are being used to ascertain almost every dental condition, ranging from the routine dental caries to the more complex conditions like oral cancer, maxillofacial cysts, alveolar bone loss, and even appraising the urgency for orthodontic extractions. Artificial intelligence, has untapped potential and shows promising prospects. Multiple studies have been evaluated to highlight the progress made till date and it has been seen that AI based automated systems are exceptional in a limited domain and perform on par to dental specialists on a variety of performance parameters. Better adaptation and utilization of technology will help in better and precise treatment outcomes, while also reducing the work burden of the clinician. Databases such as PubMed Central and Google Scholar were used to scout approximately 33 articles of recent origin using keywords such as artificial intelligence; artificial neural networks; convolutional neural networks; deep learning; and machine learning were used.

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“…Furthermore, Casalegno et al [ 17 ] combined near-infrared transillumination (TI) imaging with CNN to achieve the detection purpose by analyzing dental images. On the other hand, Aberin and de Goma [ 18 ] researched the detection of periodontal disease. The methodology of this research dwelt more on classifying the microscopic dental plaque images fed into the neural networks as healthy or unhealthy.…”

Section: Introductionmentioning

confidence: 99%

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Mao

Chen

Chou

et al. 2021

Sensors

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Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

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Detecting Periodontal Disease Using Convolutional Neural Networks

Cited by 20 publications

References 20 publications

Success of Artificial Intelligence System in Determining Alveolar Bone Loss From Dental Panoramic Radiography Images

Success of Artificial Intelligence System in Determining Alveolar Bone Loss From Dental Panoramic Radiography Images

Artificial intelligence in modern dentistry

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

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