Journal ArticleDOI
Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography
Makoto Murata,Yoshiko Ariji,Yasufumi Ohashi,Taisuke Kawai,Motoki Fukuda,Takuma Funakoshi,Yoshitaka Kise,Michihito Nozawa,Akitoshi Katsumata,Hiroshi Fujita,Eiichiro Ariji +10 more
TLDR
The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was sufficiently high and is expected to provide diagnostic support for inexperienced dentists.Abstract:
To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance. Training data for 400 healthy and 400 inflamed maxillary sinuses were enhanced to 6000 samples in each category by data augmentation. Image patches were input into a deep-learning system, the learning process was repeated for 200 epochs, and a learning model was created. Newly-prepared testing image patches from 60 healthy and 60 inflamed sinuses were input into the learning model, and the diagnostic performance was calculated. Receiver-operating characteristic (ROC) curves were drawn, and the area under the curve (AUC) values were obtained. The results were compared with those of two experienced radiologists and two dental residents. The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was high, with accuracy of 87.5%, sensitivity of 86.7%, specificity of 88.3%, and AUC of 0.875. These values showed no significant differences compared with those of the radiologists and were higher than those of the dental residents. The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was sufficiently high. Results from the deep-learning system are expected to provide diagnostic support for inexperienced dentists.read more
Citations
More filters
Journal ArticleDOI
Developments, application, and performance of artificial intelligence in dentistry - A systematic review.
Sanjeev Khanagar,Sanjeev Khanagar,Ali Al-Ehaideb,Ali Al-Ehaideb,Ali Al-Ehaideb,Prabhadevi C Maganur,Satish Vishwanathaiah,Shankargouda Patil,Hosam Ali Baeshen,Sachin C Sarode,Shilpa Bhandi +10 more
TL;DR: These studies indicate that the performance of an AI based automated system is excellent and mimic the precision and accuracy of trained specialists, in some studies it was found that these systems were even able to outmatch dental specialists in terms of performance and accuracy.
Journal ArticleDOI
Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans
TL;DR: AI systems based on deep learning methods can be useful in detecting periapical pathosis in CBCT images for clinical application and volume measurements performed by humans and by AI systems were comparable to each other.
Journal ArticleDOI
Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography
Motoki Fukuda,Kyoko Inamoto,Naoki Shibata,Yoshiko Ariji,Yudai Yanashita,Shota Kutsuna,Kazuhiko Nakata,Akitoshi Katsumata,Hiroshi Fujita,Eiichiro Ariji +9 more
TL;DR: The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.
Journal ArticleDOI
Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis.
Hyuk-Joon Chang,Sang-Jeong Lee,Tae-Hoon Yong,Nan-Young Shin,Bong-Geun Jang,Jo-Eun Kim,Kyung-Hoe Huh,Sam-Sun Lee,Min-Suk Heo,Soon-Chul Choi,Tae Il Kim,Won-Jin Yi +11 more
TL;DR: An automatic method for staging periodontitis on dental panoramic radiographs using the deep learning hybrid method and a novel hybrid framework that combined deep learning architecture and the conventional CAD approach demonstrated high accuracy and excellent reliability in the automatic diagnosis of periodontal bone loss and staging ofperiodontitis.
Journal ArticleDOI
Deep Neural Networks for Dental Implant System Classification.
Shintaro Sukegawa,Kazumasa Yoshii,Takeshi Hara,Katsusuke Yamashita,Keisuke Nakano,Norio Yamamoto,Hitoshi Nagatsuka,Yoshihiko Furuki +7 more
TL;DR: It is confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Journal ArticleDOI
Convolutional neural networks: an overview and application in radiology
TL;DR: A perspective on the basic concepts of convolutional neural network and its application to various radiological tasks is offered, and its challenges and future directions in the field of radiology are discussed.
Journal ArticleDOI
Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks
Paras Lakhani,Baskaran Sundaram +1 more
TL;DR: Deep learning with DCNNs can accurately classify TB at chest radiography with an AUC of 0.99 and an independent board-certified cardiothoracic radiologist blindly interpreted the images to evaluate a potential radiologist-augmented workflow.
Book ChapterDOI
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
Patrick Ferdinand Christ,Mohamed Ezzeldin A. Elshaer,Florian Ettlinger,Sunil Tatavarty,Marc Bickel,Patrick Bilic,Markus Rempfler,Marco Armbruster,Felix Hofmann,Melvin D'Anastasi,Wieland H. Sommer,Seyed-Ahmad Ahmadi,Bjoern H. Menze +12 more
TL;DR: In this paper, a method to automatically segment liver and lesions in CT abdomen images using cascaded fully convolutional neural networks (CFCNs) and dense 3D conditional random fields (CRFs) is presented.