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	Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography.
執筆形態	共著
掲載誌名	Oral Radiology
掲載区分	国内
著者・共著者	Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, Kise Y, Nozawa M, Katsumata A, Fujita H, Ariji E.
概要	OBJECTIVES: To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance. METHODS: Training data for 400 healthy and 400 inflamed maxillary sinuses were enhanced to 6000 samples in each category by data augmentation. Newly-prepared testing image patches from 60 healthy and 60 inflamed sinuses were input into the learning model, and the diagnostic performance was calculated. RESULTS: 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. CONCLUSIONS: Results from the deep-learning system are expected to provide diagnostic support for inexperienced dentists.