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
TLDR
The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.Abstract:
The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography. Three hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected from our hospital imaging database. Confirmation of VRF lines was performed by two radiologists and one endodontist. Eighty percent (240 images) of the 300 images were assigned to a training set and 20% (60 images) to a test set. A CNN-based deep learning model for the detection of VRFs was built using DetectNet with DIGITS version 5.0. To defend test data selection bias and increase reliability, fivefold cross-validation was performed. Diagnostic performance was evaluated using recall, precision, and F measure. Of the 330 VRFs, 267 were detected. Twenty teeth without fractures were falsely detected. Recall was 0.75, precision 0.93, and F measure 0.83. The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.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
Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs
André Ferreira Leite,André Ferreira Leite,Adriaan Van Gerven,Holger Willems,Thomas Beznik,Pierre Lahoud,Hugo Gaêta-Araujo,Hugo Gaêta-Araujo,Myrthel Vranckx,Reinhilde Jacobs,Reinhilde Jacobs +10 more
TL;DR: An innovative clinical AI-driven tool showed a faster and more accurate performance to detect and segment teeth on panoramic radiographs compared with manual segmentation, faster than the ground truth alone.
Journal ArticleDOI
A deep learning approach for dental implant planning in cone-beam computed tomography images.
Sevda Kurt Bayrakdar,Kaan Orhan,Ibrahim Sevki Bayrakdar,Elif Bilgir,Matvey Ezhov,Maxim Gusarev,Eugene Shumilov +6 more
TL;DR: Development of AI systems and their using in future for implant planning will both facilitate the work of physicians and will be a support mechanism in implantology practice to physicians.
Journal ArticleDOI
Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs.
Chiaki Kuwada,Yoshiko Ariji,Motoki Fukuda,Yoshitaka Kise,Hiroshi Fujita,Akitoshi Katsumata,Eiichiro Ariji +6 more
TL;DR: DetectNet and AlexNet appear to have potential use in classifying the presence of ISTs in the maxillary incisor region on panoramic radiographs and would be suitable for automatic detection of this abnormality.
Journal ArticleDOI
Performance of deep learning object detection technology in the detection and diagnosis of maxillary sinus lesions on panoramic radiographs.
Ryosuke Kuwana,Yoshiko Ariji,Motoki Fukuda,Yoshitaka Kise,Michihito Nozawa,Chiaki Kuwada,Chisako Muramatsu,Akitoshi Katsumata,Hiroshi Fujita,Eiichiro Ariji +9 more
TL;DR: This study indicated that the detection sensitivities of maxillary sinuses were high and the performance ofmaxillary sinus lesion identification was ≧80%.
References
More filters
Posted Content
Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation
TL;DR: E elegant connections between the concepts of Informedness, Markedness, Correlation and Significance as well as their intuitive relationships with Recall and Precision are demonstrated.
Journal ArticleDOI
Object Detection With Deep Learning: A Review
TL;DR: In this article, a review of deep learning-based object detection frameworks is provided, focusing on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further.
Journal ArticleDOI
Large scale deep learning for computer aided detection of mammographic lesions
Thijs Kooi,Geert Litjens,Bram van Ginneken,Albert Gubern-Mérida,Clara I. Sánchez,Ritse M. Mann,Ard den Heeten,Nico Karssemeijer +7 more
TL;DR: A head‐to‐head comparison between a state‐of‐the art in mammography CAD system, relying on a manually designed feature set and a Convolutional Neural Network (CNN), aiming for a system that can ultimately read mammograms independently.
Journal ArticleDOI
Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks
Moi Hoon Yap,Gerard Pons,Joan Martí,Sergi Ganau,Melcior Sentís,Reyer Zwiggelaar,Adrian K. Davison,Robert Martí +7 more
TL;DR: This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet.
Journal ArticleDOI
Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm.
TL;DR: A deep CNN algorithm provided considerably good performance in detecting dental caries in periapical radiographs, and is expected to be among the most effective and efficient methods for diagnosing dental carie.