arXiv e-print 2017;arXiv:1, convolutional neural networks. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm Received: Mar 19, 2018 Accepted: Apr 23, 2018 *Correspondence: Jae-Hong Lee Department of Periodontology, Daejeon Dental Hospital, Wonkwang University College of Dentistry, 77 Dunsan-ro, Seo-gu, Daejeon 35233, Korea. females. Case and control groups matched for gender, age, household income, type of social security, disability, and residential area were generated. (n=348), and test (n=348) datasets. In our previous studies, we demonstrated that the pre-trained DCNN using dental radiographic images demonstrated high accuracy in identifying and classifying periodontally compromised teeth (AUC = 0.781, 95% CI = 0.650-0.87.6) and dental caries (AUC = 0.845, 95% CI = 0.790-0.901) at a level equivalent to that of experienced dental professionals. There are several studies that show pathogenic correlation between tooth position in the dental arch and periodontal condition . avoid overtting and to normalize the model [34]. In the multivariate Cox proportional-hazard regression analysis with adjustment for confounding factors, PD was associated with a 14% higher risk of PC (HR = 1.14, 95% CI = 1.01-1.31, P = 0.042). Two Faster Region-based Convolutional Neural Network (R-CNN) models using ResNet-50 Convolutional Neural Network (CNN) were developed. The deep learning model demonstrated a high sensitivity of 97.6% [95% confidence interval (CI), 94.2-100%] and a high specificity of 96.5% (95% CI, 90.2-100%), and the area under the curve was 0.988 (95% CI, 0.981-0.995). Overall architecture of the deep CNN model. Secondary occlusal traumais usually associated with a periodontally compromised dentition that has resulted in severe bone loss and teeth with adverse crown-to-root ratios. Medicine (Baltimore) 2015;94:e1567. Int J Environ Res Public Health. The deep CNN system (CranioCatch, Eskisehir, Turkey) was used to detect and number teeth in bitewing radiographs. Clinical and microbiological parameters were evaluated. Purpose: The test dataset was used to calculate the chi-square, diagnostic, value, receiver operating characteristic (R, confusion matrix using our deep CNN algorithm, based on a Keras framework in Python, values of less than 0.05 were considered to indicate, The baseline characteristics of the study population are presented in Table 1. Diagnosis and prediction of prognosis of periodontally compromised teeth from periapical radiograph were successfully studied. Medicine (Baltimore) 2015;94:e1567 The study included 1125 bite-wing radiographs of patients who attended the Faculty of Dentistry of Ordu University from 2018 to 2019. Combining pretrained deep CNN architecture and a self-trained network, The periapical radiographic dataset was split into training (n=1,044), validation. Because the dierential diagnosis between, healthy teeth and incipient PCT was made using only periapical radiographs, this study did not. Steel workers are a special occupational group. The diagonal elements are the number of points where the, predicted label was the same as the actual label, while the non-diagonal elements were, misinterpreted by the classier. This structure is distinguished from, conventional image classication algorithms and other deep learning algorithms, since CNN, can learn the type of lter that is hand-, the same padding, and a rectied linear unit activation function. Prosthetic joint infection, Evolution of periodontal disease is one of the most important data for This article focuses on the application of machine learning techniques in the field of stomatology and detailedly describes application cases involving oral cancer, dental caries, periodontitis, dental pulp diseases, periapical lesions, oral implants, and orthodontics. Implant placement in periodontally compromised patients has been evaluated in the literature. CNN is a type of machine learning that is used in various elds, especially in image and, sound recognition. DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network. those obtained by board-certied periodontists. Women with periodontitis were more likely to also develop osteoporosis (HR: 1.22, 95% CI: 1.01–1.48). Cancers (Basel). Methods severely periodontally compromised.11 Moderately and se-verely periodontally compromised teeth were grouped to-gether to form the periodontally compromised teeth group. Keywords: gingival recession, poor prognosis, multi-disciplinary, pink composite, Free Gingival Graft Introduction A beautiful smile goes a long way in boosting the personality and confidence of a person. Medicine, osteoporosis: results from a nationwide population-based cohort study (2003-2013). In: Journal of Periodontal and Implant Science, Vol. Removable and fixed periodontal prostheses were taught in dental schools and post-graduate programs. Results: prognostic judgment depends heavily on empirical evidence [11]. This prolongs the life expectancy of loose teeth, gives stability for the periodontium to reattach, and improves comfort, function and aesthetics. Daejeon Dental Hospital, Wonkwang University College of Dentistry, The aim of the current study was to develop a computer-assisted detection system. The automated DCNN outperformed most of the participating dental professionals, including board-certified periodontists, periodontal residents, and residents not specialized in periodontology. Every attempt should be made to minimize this problem. Treating and maintaining periodontally diseased teeth of questionable prognosis has to be put in perspective with the increased risk for future peri-implantitis and possible implant failure, which could jeopardize prosthetic success. This randomized controlled clinical trial evaluated the effects of an adjunctive single application of antimicrobial photodynamic therapy (aPDT) in Surgical Periodontal Treatment (ST) in patients with severe chronic periodontitis (SCP). A tot, weights were learned using the Adam algorithm (learning rate=0.0001), a stochastic gradient, of this training phase, ne-tuning was performed in order to optimize the weights and to, improve the results by adjusting the hyperparameters of layers [, A randomization sequence was generated using the RAND function in the Excel spreadsheet, image dataset into a training dataset (n=1,044; 60%), a validat, and a test dataset (n=348; 20%). HHS The training and validation dataset, CNN algorithm model. BMC Oral Health 2016;16:118. due to periodontal disease: results of a 12-year longitudinal cohort study in South Korea. In another study of the diagnosis of skin cancer, 18 doctors systematically. J Periodontal Implant Sci 2017;47:96-, communicable diseases: A 12-year longitudinal health-examinee cohort study in South Korea. BMC W. cancer: results of a 12-year longitudinal cohort study in South Korea. In particular, the deep CNN algorithm has been used most commonly and, based on the CNN algorithm, was performed using a prelabeled periapical radiographic, dataset. 2020 Nov 7;10(11):910. doi: 10.3390/diagnostics10110910. with periodontally compromised teeth having poor prognosis. Teeth with worse prognosis have a worse survival rate, but the commonly taught clinical parameters used in the traditional method of assignment of prognosis do not adequately explain that relationship. To compare the accuracy of the trained automated DCNN with dental professionals (including six board-certified periodontists, eight periodontology residents, and 11 residents not specialized in periodontology), 180 images were randomly selected from the test dataset. USA.gov. Conclusions: ARTIFICIAL INTELLIGENCE IN DENTISTRY: WHERE ARE WE NOW AND WHERE ARE WE HEADING NEXT? Develop and Evaluate a New and Effective Approach for Predicting Dyslipidemia in Steel Workers. Usually, the teeth are very mobile; therefore, the teeth are subjected to continued injury with normal forces such as mastication or deglutition, or both. Consistent patient follow-up is required to observe changes in trends regarding tooth extraction according to changes in dental healthcare policies, and meticulous studies of such changes will ensure optimal policy reviews and revisions. Methods: The detection accuracy, precision, recall, and mean average precision (mAP) were calculated to verify the significance of the proposed model. Is periodontal disease related to preeclampsia? must be done periodically study bone resorption evolution around teeth. Radiological examination has an important place in dental practice, and it is frequently used in intraoral imaging. A case-control study was carried out on 26 pure preeclamptic women and 25 women with normal pregnancy. The Area Under the Curve (AUC) was 0.912 with a sensitivity of 86% and a specificity of 76%. T, examination and a CAL of less than 6 mm or a bone loss of less than 4 mm on radiography, were classied as moderate PCT, and teeth with a CAL of greater than 6 mm and a bone loss, of more than 4 mm were classied as severe PCT [, extracted immediately aer clinical and radiological examinations or during the follow-up, period of 3 months were dened as hopeless teeth. Pairwise comparison between the deep CNN algorithm and periodontists for the prediction of hopeless teeth, based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential, usefulness and accuracy of this system for the diagnosis and prediction of periodontally, periapical radiographic images were used to determine the optimal CNN algorithm and, weights. Results Association of lifestyle-related comorbidities with periodontitis: a nationwide cohort study in Korea. Creative Commons Attribution-NonCommercial 4.0 International, A Performance Comparison between Automated Deep Learning and Dental Professionals in Classification of Dental Implant Systems from Dental Imaging: A Multi-Center Study, A scoping review of transfer learning research on medical image analysis using ImageNet, A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks, An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs, Application of Machine Learning to Stomatology: A Comprehensive Review. MATERIALS AND METHODS: Total 200 patients took part in the questionnaire based study and were examined using the PSI. aggressiveness of periodontitis. survival, better prognosis of implants supported restorations, better functioning than periodontally compromised teeth, improved esthetics and cost effective with increased patient satisfaction [3]. Time factor of resorption indicates, Several studies have hypothesized that periodontal diseases may increase the risk of preeclampsia. Periodontal probing depth, clinical attachment level as well as bleeding upon probing and supragingival plaque was assessed at 6 sites of every tooth present. This longitudinal cohort study has provided evidence that patients with PD are at increased risk of NCDs. Develop and Evaluate a New and Effective Approach for Predicting Dyslipidemia in Steel Workers, Effectiveness of Artificial Intelligence Applications Designed for Endodontic Diagnosis, Decision-making, and Prediction of Prognosis: A Systematic Review, Detection and Classification of Dental Pathologies using Faster-RCNN in Orthopantomogram Radiography Image, Trends in the incidence of tooth extraction due to periodontal disease: Results of a 12-year longitudinal cohort study in South Korea, Association between Periodontal disease and Prostate cancer: Results of a 12-year Longitudinal Cohort Study in South Korea, Effect of periodontitis on the development of osteoporosis: Results from a nationwide population-based cohort study (2003-2013), Accuracy of deep learning, a machine-learning technology, using ultra–wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment, Association between periodontal disease and non-communicable diseases: A 12-year longitudinal health-examinee cohort study in South Korea, Association between periodontal flap surgery for periodontitis and vasculogenic erectile dysfunction in Koreans, TensorFlow : Large-Scale Machine Learning on Heterogeneous Distributed Systems. For patients who require removal of anterior teeth and their replacement various treatment modalities are available. Each of the convolutional layers is followed by a ReLU activation function, dropout, maximum pooling layers, and 3 fully connected layers with 1,024, 1,024, and 512 nodes, respectively. that are only limited for University researchers. Wu J, Qin S, Wang J, Li J, Wang H, Li H, Chen Z, Li C, Wang J, Yuan J. The first model detects the teeth to locate the region of interest (ROI), while the second model detects gingival inflammation. The data from each subject were reported in mean and finally the average amount of each group was compared to others and analyzed using SPSS software, t-test, and Mann-Whitney test. Patients with advanced periodontal disease may experience tooth migration involving single or multiple teeth. It was concluded that there was high interindividual and intraindividual variation of the relative risk for bleeding in the presence of plaque. Conference on Computer Vision (ICCV); 2015 Dec 7–, ... A DCNN that is specifically designed for detection, classification, and segmentation in vision tasks and practical applications has been rapidly exploited in recent years in conjunction with improvements in computer performance and deep learning techniques [12]. Dropout, which is a typical method of regularization (rescaling the deep CNN weights, to a more eective range), was set to 0.5, and the nal output layer was classied in terms of, PCT using the Somax classier [22]. Conflict of Interest: No potential conflict of interest relevant to this article was reported. J Periodontal Implant Sci. 2018 Apr;48(2):114-123. https://doi.org/10.5051/jpis.2018.48.2.114  |  / Lee, Jae Hong; Kim, Do Hyung; Jeong, Seong Nyum; Choi, Seong Ho. Every convolutional layer responds to stimuli only in a restricted region, of the visual eld known as the receptive eld. There were no statistical differences between groups with regard to mean clinical attachment loss (P = 0.16), mean gingival bleeding (P = 0.89), and mean plaque (P = 0.95) indices. The current study aimed to develop and evaluate the state-of-the-art object detection and recognition techniques and deep learning algorithms for the automatic detection of periodontal disease in orthodontic patients using intraoral images. J Clin Periodontol. In dentistry, CNNs are used clinically for caries detection [10,11], apical lesion detection [12,13], diagnosis of jaw lesion [14,15], detection of periodontal disease. Aim: Data indicate the presence of a lesion prior to treatment only decreases the prognosis slightly. Furthermore, initial prognosis did not adequately explain the condition of the tooth or accurately predict the tooth's survival. We aimed to conduct a scoping review to identify these studies and summarize their characteristics in terms of the problem description, input, methodology, and outcome. In addition, all the, 19 network was used for preprocessing, and the dataset was augmented using the Keras, framework based on the ImageDataGenerator f, with a rotation range of 15°, a width and height shi range of 0.1, a shear range of 0.5, and 100. images were generated for each tooth to obtain a total of 104,400 training dataset images. This site needs JavaScript to work properly. In 2002, 50.6% of cases of TE were caused by PD, and this increased to 70.8% in 2013, while the number of cases of IEPD increased from 42.8% to 54.9% over the same period. Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study. This architecture, which consists of 16 convo, connected layers, is ideal for deep learning and very eective at solving object detection and. 2020 Sep 10;8:839. doi: 10.3389/fbioe.2020.00839. Then, based on the data characteristics, the corresponding parameters were set for the convolutional neural network model, and the risk of dyslipidemia in steel workers was predicted by using convolutional neural network. Proceedings of SPIE - The International Society for Optical Engineering. Diagnosis and Prediction of Periodontally Compromised Teeth Using a Deep Learning-Based Convolutional Neural Network Algorithm - PubMed We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Materials and methods Introduction Definition Periodontal Splints Margin Placement Attached gingiva Restoration of molar teeth with furcation invasion Conclusion Fixed Prosthodontics in Periodontally Compromised Dentitions 4. Conclusions Share to Twitter Share to Facebook Share to Pinterest. Periodontitis was not associated with the development of osteoporosis in males. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%–91.2%) for premolars and 73.4% (95% CI, 59.9%–84.0%) for molars. Thus, early diagnosis and treatment of RRD is crucial. The incidence of osteoporosis was 1.1% in males and 15.8% in females during a 10-year period. Tonetti MS, Jepsen S, Jin L, Otomo-Corgel J. Conclusions 2020 Jul 1;10(7):984. doi: 10.3390/biom10070984. For molars, the total, diagnostic accuracy was 76.7%, the diagnostic accuracy was the highest for severe PCT. In terms of age, the number of individuals in their 20s was the smallest (n=22; 3.4%), and the number of those in their 60s was the highest (n=216; 33.2%). This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. the clinicians in order to achieve correct planning and treatment. We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. This study aimed to propose an automatic detection system for the numbering of teeth in bitewing images using a faster Region-based Convolutional Neural Networks (R-CNN) method. Radiology 2017;284:57, Advances in neural networks – ISNN 2016. Int Dent J 1982;32:281-91. convolutional neural networks. A 3-dimensional deep CNN. Finally, the predictive performance of the convolutional neural network model is compared with the existing predictive models of dyslipidemia, logistics regression model and BP neural network model. Med Image Anal 2017;42:60-88. periodontal patients. arXiv e-print 2016:arXiv:1603.04467, screening mammography with and without computer-aided detection. The distribution of the individual total score exhibited a high statistical significance (p<0.001) of robustness in terms of differing definitions of periodontitis. Objective This study evaluated trends in tooth extraction due to acute and chronic periodontal disease (PD) using data from the National Health Insurance Service-National Sample Cohort for 2002–2013. The automated DCNN was highly effective in classifying similar shapes of different types of DISs based on dental radiographic images. All patients were monitored until 90 days after surgical therapy. Good Morning 2. 1-4 Tooth mobility, however, can be controlled and managed with splinting therapy. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. affect both preeclampsia and periodontal conditions. The final sample included 13,464 participants. In light of the observations of many astute clinicians, developing a treatment plan that will provide the best long-term prognosis is a challenge in the advanced periodontally involved dentition. This study proved the viability of deep learning models for the detection and diagnosis of gingivitis in intraoral images. deep CNN algorithms for diagnosing and predicting PCT. 24-11-15 & 1-12-15 3. Results J Periodontal Implant Sci. The presence of periapical radiolucency is not an absolute indicator of a poor long-term prognosis. Univariate and multivariate logistic regression analyses adjusting for potential confounders during the follow-up period—including age, sex, household income, insurance status, residence area, health status, and comorbidities—were used to estimated odds ratios (ORs) with 95% confidence intervals (CIs) in order to assess the associations between PD and NCDs. A total of 651, subjects participated in the present study, consis. Sci, 27th International Conference on Internation, learning on heterogeneous distributed systems. Prior to the ubiquitous use of implants, many periodontally diseased teeth were retained through frequent recalls and heroic treatment efforts. CLINICAL RELEVANCE: Patient-based data (clinical variables and periodontal risk factors of periodontitis) were adequate to make a preliminary assessment of a possible need for periodontal treatment. (81.3%), and the diagnostic accuracy was the lowest for moderate PCT (70.3%). BMC Oral Health. tain prognosis, periodontally compromised teeth with deeper pockets than 5-6 mm, teeth with fur-cation involvement or endoperiodontal lesions, teeth with periapical lesions and teeth with a root canal are technically difficult or with uncertain prognosis and teeth with very deep or extensive caries. Advanced bone defects, deep pockets, and tooth mobility are found to be associated with increased risk of tooth loss. Results W, eciently perform edge detection with only 2 convolutional layers and 1 fully connected hidden, detection problem, it was chosen for use in the present study [, The deep CNN algorithm used in the current study was designed based on the VGG-, network architecture. Conclusion: Clinical measure of the periodontal sulcus depth is the most important 35% of the studies compared their model with other well-trained CNN models and 33% of them provided visualization for interpretation. After screening of 8421 articles, 102 met the inclusion criteria. 13th International Symposium on Neural Networks, ISNN, vision. Conclusions The inflammation detection model achieved an accuracy, precision, recall, and mAP of 77.12%, 88.02%, 41.75%, and 68.19%, respectively. A CNN approach for the analysis of bitewing images shows promise for detecting and numbering teeth. (95% CI, 70.1%–91.2%) for premolars and 73.4% (95% CI, 59.9%–84.0%) for molars. The National Health Insurance Service–Health Examinee Cohort during 2002 to 2013 was used to investigate the associations between periodontal disease (PD) and the following non-communicable diseases (NCDs): hypertension, diabetes mellitus, osteoporosis, cerebral infarction, angina pectoris, myocardial infarction, and obesity. When the alveolar bone loss excessively horizontally, the teeth will be loosened. The dataset for the PCT images…, Figure 2. Thr, factors complicate this task. Comput Biol Med 2016;68:37. medical image analysis. However, controversy persists as to its impact on diagnosis and treatment planning. Formulating a global prognosis and treatment plan for the periodontally compromised patient: a reconstructive vs. an adaptive approach. Most studies on brain MRI images [119][120][121]123] as well as breast X-Ray [114][115][116]118] images obtained adequate performance with AlexNet, which may indicate that shallow CNN models with large kernel sizes are optimal for those problems. 2020 Jun 26;99(26):e20787. AIM: To evaluate, a self-reported questionnaire about periodontal risk factors in combination with the Periodontal Screening Index (PSI) to identify an existing need for periodontal treatment combined with the early recognition of high-risk patients. TensorFlow [1] is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. J Clin Periodontol 2017;44:717. 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