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 2019/05
形態種別 学術雑誌
査読 査読あり
標題 Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence.
執筆形態 共著
掲載誌名 Oral Surg Oral Med Oral Pathol Oral Radiol
掲載区分国外
著者・共著者 Ariji Y, Fukuda M, Kise Y, Nozawa M, Yanashita Y, Fujita H, Katsumata A, Ariji E.
概要 OBJECTIVE:
The purpose of this study was to evaluate the performance of deep learning image classification for diagnosis of lymph node metastasis.
STUDY DESIGN:
The imaging data used for evaluation consisted of computed tomography (CT) images of 127 histologically proven positive cervical lymph nodes and 314 histologically proven negative lymph nodes from 45 patients with oral squamous cell carcinoma.
RESULTS:
The performance of the deep learning image classification system resulted in accuracy of 78.2%, sensitivity of 75.4%, specificity of 81.0%, positive predictive value of 79.9%, negative predictive value of 77.1%, and area under the receiver operating characteristic curve of 0.80. These values were not significantly different from those found by the radiologists.
CONCLUSIONS:
The deep learning system yielded diagnostic results similar to those of the radiologists, which suggests that this system may be valuable for diagnostic support.