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	Preliminary study on the application of deep learning system to diagnosis of Sjogren's syndrome on CT images.
執筆形態	共著
掲載誌名	Dentomaxillofacal Radiology
掲載区分	国外
著者・共著者	Kise Y, Ikeda H, Fujii T, Fukuda M, Ariji Y, Fujita H, Katsumata A, Ariji E.
概要	OBJECTIVES: This study estimated the diagnostic performance of a deep learning system for detection of Sjögren's syndrome (SjS) on CT. METHODS: CT images were assessed from 25 patients confirmed to have SjS based on the both Japanese criteria and American-European Consensus Group criteria and 25 control subjects with no parotid gland abnormalities who were examined for other diseases. 10 CT slices were obtained for each patient. RESULTS: The accuracy, sensitivity, and specificity of the deep learning system were 96.0%, 100% and 92.0%, respectively. The corresponding values of experienced radiologists were 98.3%, 99.3% and 97.3% being equivalent to the deep learning, while those of inexperienced radiologists were 83.5%, 77.9% and 89.2%. CONCLUSIONS: The deep learning system showed a high diagnostic performance for SjS, suggesting that it could possibly be used for diagnostic support when interpreting CT images.