Motoki Fukuda1, Kyoko Inamoto2, Naoki Shibata2, Yoshiko Ariji3, Yudai Yanashita4, Shota Kutsuna4, Kazuhiko Nakata2, Akitoshi Katsumata5, Hiroshi Fujita4, Eiichiro Ariji3. 1. Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan. halpop@dpc.agu.ac.jp. 2. Department of Endodontics, Aichi-Gakuin University School of Dentistry, Nagoya, Japan. 3. Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan. 4. Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan. 5. Department of Oral Radiology, Asahi University, Mizuho, Japan.
Abstract
OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.
OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.
Authors: Paulo Raphael Leite Maia; Ana Miryam C Medeiros; Hallissa S G Pereira; Kenio C Lima; Patrícia T Oliveira Journal: Oral Surg Oral Med Oral Pathol Oral Radiol Date: 2018-04-24
Authors: Manal H Hamdan; Lyudmila Tuzova; André Mol; Peter Z Tawil; Dmitry Tuzoff; Donald A Tyndall Journal: Dentomaxillofac Radiol Date: 2022-09-12 Impact factor: 3.525