フクダ モトキ   HUKUDA Motoki   福田 元気    所属   歯学部 歯科放射線学    職種   助教
言語種別	英語
発行・発表の年月	2019/09
形態種別	学術雑誌
査読	査読あり
標題	Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography.
執筆形態	共著
掲載誌名	Oral Radiology
掲載区分	国外
著者・共著者	Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, Nakata K, Katsumata A, Fujita H, Ariji E.
概要	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: 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.