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 2019/03
形態種別 学術雑誌
査読 査読あり
標題 A deep learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography.
執筆形態 共著
掲載誌名 Dentomaxillofac Radiol
掲載区分国外
著者・共著者 Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, Fujita H, Ariji E.
概要 OBJECTIVES: In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs.
METHODS: CBCT images and panoramic radiographs of 760 mandibular first molars from 400 patients who had not undergone root canal treatments were analyzed. Distal roots were examined on CBCT images to determine the presence of a single or extra root. Image patches of the roots were segmented from panoramic radiographs and applied to a deep learning system, and its diagnostic performance in the classification of root morphplogy was examined.
RESULTS: The deep learning system had diagnostic accuracy of 86.9% for the determination of whether distal roots were single or had extra roots.
CONCLUSIONS: The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.