Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography
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ORIGINAL ARTICLE
Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography Motoki Fukuda1 · Kyoko Inamoto2 · Naoki Shibata2 · Yoshiko Ariji1 · Yudai Yanashita3 · Shota Kutsuna3 · Kazuhiko Nakata2 · Akitoshi Katsumata4 · Hiroshi Fujita3 · Eiichiro Ariji1 Received: 26 June 2019 / Accepted: 31 August 2019 © Japanese Society for Oral and Maxillofacial Radiology and Springer Nature Singapore Pte Ltd. 2019
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. Keywords Panoramic radiography · Vertical root fracture · Artificial intelligence · Deep learning · Object detection
Introduction Panoramic radiography is frequently used for screening for various abnormalities of the jaws and their adjacent structures, and is recognized as a reliable and convenient technique [1]. However, because of the complexity of the Motoki Fukuda and Kyoko Inamoto contributed equally to this work * Motoki Fukuda [email protected] 1
Department of Oral and Maxillofacial Radiology, AichiGakuin University School of Dentistry, 2‑11 Suemori‑dori, Chikusa‑ku, Nagoya 464‑8651, Japan
2
Department of Endodontics, Aichi-Gakuin University School of Dentistry, Nagoya, Japan
3
Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan
4
Department of Oral Radiology, Asahi University, Mizuho, Japan
relationships between anatomical structures and the panoramic image layer, panoramic radiography images may sometimes be difficult to interpret, especially for inexperienced observers, with the result that critical disease may be overlooked [2]. In this regard, a number of computer-assisted detection/diagnosis (CAD) systems have been developed for various diseases, including maxillary sinusitis [3], osteoporosis [4, 5], and carotid artery calcification [6]. In these systems, image characteristics that are extracted by experienced human observers are input into the CAD system for diagnostic assistance. More rec
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