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Journal ArticleDOI: 10.1259/DMFR.20200172

Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs

04 Mar 2021-Dentomaxillofacial Radiology (The British Institute of Radiology.)-Vol. 50, Iss: 6, pp 20200172-20200172
Abstract: Objective:This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs.Methods and materials:An...

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Topics: Deciduous teeth (53%)
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6 results found


Open accessJournal ArticleDOI: 10.3390/BIOM11060815
30 May 2021-
Abstract: It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020 Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification Using CNNs conferred high validity in the classification of dental implant brands and treatment stages Furthermore, multi-task learning facilitated analysis accuracy

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Topics: F1 score (53%), Dental implant (50%)

1 Citations


Journal ArticleDOI: 10.1259/DMFR.20210296
Mohamed Estai1, Mohamed Estai2, Marc Tennant2, Dieter Gebauer2  +5 moreInstitutions (3)
Abstract: Objective:This study aimed to evaluate an automated detection system to detect and classify permanent teeth on orthopantomogram (OPG) images using convolutional neural networks (CNNs).Methods:In to...

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Journal ArticleDOI: 10.1016/J.JDENT.2021.103865
Abstract: Objectives Automatic tooth segmentation and classification from cone beam computed tomography (CBCT) have become an integral component of the digital dental workflows. Therefore, the aim of this study was to develop and validate a deep learning approach for an automatic tooth segmentation and classification from CBCT images. Methods A dataset of 186 CBCT scans was acquired from two CBCT machines with different acquisition settings. An artificial intelligence (AI) framework was built to segment and classify teeth. Teeth were segmented in a three-step approach with each step consisting of a 3D U-Net and step 2 included classification. The dataset was divided into training set (140 scans) to train the model based on ground-truth segmented teeth, validation set (35 scans) to test the model performance and test set (11 scans) to evaluate the model performance compared to ground-truth. Different evaluation metrics were used such as precision, recall rate and time. Results The AI framework correctly segmented teeth with optimal precision (0.98±0.02) and recall (0.83±0.05). The difference between the AI model and ground-truth was 0.56±0.38 mm based on 95% Hausdorff distance confirming the high performance of AI compared to ground-truth. Furthermore, segmentation of all the teeth within a scan was more than 1800 times faster for AI compared to that of an expert. Teeth classification also performed optimally with a recall rate of 98.5% and precision of 97.9%. Conclusions The proposed 3D U-Net based AI framework is an accurate and time-efficient deep learning system for automatic tooth segmentation and classification without expert refinement. Clinical significance The proposed system might enable potential future applications for diagnostics and treatment planning in the field of digital dentistry, while reducing clinical workload.

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Open accessJournal ArticleDOI: 10.1007/S11282-021-00577-9
Ibrahim Sevki Bayrakdar1, Kaan Orhan2, Serdar Akarsu1, Özer Çelik1  +8 moreInstitutions (4)
22 Nov 2021-Oral Radiology
Abstract: The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer. A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively. The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists. CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry.

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Journal ArticleDOI: 10.1259/DMFR.20210246
Abstract: Objectives:The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images.Methods...

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Topics: Deep learning (55%), Convolutional neural network (54%), Numbering (52%)

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35 results found


Open accessJournal ArticleDOI: 10.1038/S41598-019-40068-W
Tian-You Cheng1, Jiun-Haw Lee2, Chia-Hsun Chen2, Po-Hsun Chen2  +11 moreInstitutions (4)
06 Mar 2019-Scientific Reports
Abstract: In this study, we demonstrated a blue phosphorescent organic light-emitting diode (BPOLED) based on a host with two carbazole and one trizole (2CbzTAZ) moiety, 9,9′-(2-(4,5-diphenyl-4H-1,2,4-triazol-3-yl)-1,3-phenylene)bis(9H-carbazole), that exhibits bipolar transport characteristics. Compared with the devices with a carbazole host (N,N’-dicarbazolyl-3,5-benzene, (mCP)), triazole host (3-(biphenyl-4-yl)-5-(4-tert-butylphenyl)-4-phenyl-4H-1,2,4-triazole, (TAZ)), or a physical mixture of mCP:TAZ, which exhibit hole, electron, and bipolar transport characteristics, respectively, the BPOLED with the bipolar 2CbzTAZ host exhibited the lowest driving voltage (6.55 V at 10 mA/cm2), the highest efficiencies (maximum current efficiency of 52.25 cd/A and external quantum efficiency of 23.89%), and the lowest efficiency roll-off, when doped with bis[2-(4,6-difluorophenyl)pyridinato-C2,N](picolinato)iridium(III) (FIrpic) as blue phosphor. From analyses of light leakage of the emission spectra of electroluminescence, transient electroluminescence, and partially doped OLEDs, it was found that the recombination zone was well confined inside the emitting layer and the recombination rate was most efficient in a 2CbzTAZ-based OLED. For the other cases using mCP, TAZ, and mCP:TAZ as hosts, electrons and holes transported with different routes that resulted in carrier accumulation on different organic molecules and lowered the recombination rate.

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Topics: Phosphorescent organic light-emitting diode (59%), Electroluminescence (52%), Carbazole (51%) ... show more

359 Citations


Journal ArticleDOI: 10.1016/J.JDENT.2018.07.015
Abstract: Objectives Deep convolutional neural networks (CNNs) are a rapidly emerging new area of medical research, and have yielded impressive results in diagnosis and prediction in the fields of radiology and pathology. The aim of the current study was to evaluate the efficacy of deep CNN algorithms for detection and diagnosis of dental caries on periapical radiographs. Materials and methods A total of 3000 periapical radiographic images were divided into a training and validation dataset (n = 2400 [80%]) and a test dataset (n = 600 [20%]). A pre-trained GoogLeNet Inception v3 CNN network was used for preprocessing and transfer learning. The diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, and area under the curve (AUC) were calculated for detection and diagnostic performance of the deep CNN algorithm. Results The diagnostic accuracies of premolar, molar, and both premolar and molar models were 89.0% (80.4–93.3), 88.0% (79.2–93.1), and 82.0% (75.5–87.1), respectively. The deep CNN algorithm achieved an AUC of 0.917 (95% CI 0.860–0.975) on premolar, an AUC of 0.890 (95% CI 0.819–0.961) on molar, and an AUC of 0.845 (95% CI 0.790–0.901) on both premolar and molar models. The premolar model provided the best AUC, which was significantly greater than those for other models (P Conclusions This study highlighted the potential utility of deep CNN architecture for the detection and diagnosis of dental caries. A deep CNN algorithm provided considerably good performance in detecting dental caries in periapical radiographs. Clinical significance Deep CNN algorithms are expected to be among the most effective and efficient methods for diagnosing dental caries.

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168 Citations


Journal ArticleDOI: 10.1016/J.COMPBIOMED.2016.11.003
Yuma Miki1, Chisako Muramatsu1, Tatsuro Hayashi, Xiangrong Zhou1  +3 moreInstitutions (2)
Abstract: Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic filing of dental charts for forensic identification. A new application of deep convolutional neural network to dental images was explored.Good classification performance was obtained even with a small number of data.Data augmentation, especially the intensity transformation was effective in improving the classification performance.Performance was not strongly dependent on resizing methods except for the crop method.DCNN was effective in tooth classification without the need for precise tooth segmentation

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145 Citations


Open accessJournal ArticleDOI: 10.1002/JMRI.26534
Abstract: Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep-learning algorithms. We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review a broad range of deep-learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:939-954.

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Topics: Deep learning (56%)

145 Citations


Open accessJournal ArticleDOI: 10.1016/J.EJRAD.2019.02.038
Abstract: The advent of Deep Learning (DL) is poised to dramatically change the delivery of healthcare in the near future. Not only has DL profoundly affected the healthcare industry it has also influenced global businesses. Within a span of very few years, advances such as self-driving cars, robots performing jobs that are hazardous to human, and chat bots talking with human operators have proved that DL has already made large impact on our lives. The open source nature of DL and decreasing prices of computer hardware will further propel such changes. In healthcare, the potential is immense due to the need to automate the processes and evolve error free paradigms. The sheer quantum of DL publications in healthcare has surpassed other domains growing at a very fast pace, particular in radiology. It is therefore imperative for the radiologists to learn about DL and how it differs from other approaches of Artificial Intelligence (AI). The next generation of radiology will see a significant role of DL and will likely serve as the base for augmented radiology (AR). Better clinical judgement by AR will help in improving the quality of life and help in life saving decisions, while lowering healthcare costs. A comprehensive review of DL as well as its implications upon the healthcare is presented in this review. We had analysed 150 articles of DL in healthcare domain from PubMed, Google Scholar, and IEEE EXPLORE focused in medical imagery only. We have further examined the ethic, moral and legal issues surrounding the use of DL in medical imaging.

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135 Citations


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