論文

査読有り
2019年4月

Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography.

Journal of gastroenterology
  • Ren Togo
  • ,
  • Nobutake Yamamichi
  • ,
  • Katsuhiro Mabe
  • ,
  • Yu Takahashi
  • ,
  • Chihiro Takeuchi
  • ,
  • Mototsugu Kato
  • ,
  • Naoya Sakamoto
  • ,
  • Kenta Ishihara
  • ,
  • Takahiro Ogawa
  • ,
  • Miki Haseyama

54
4
開始ページ
321
終了ページ
329
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1007/s00535-018-1514-7

BACKGROUND: Deep learning has become a new trend of image recognition tasks in the field of medicine. We developed an automated gastritis detection system using double-contrast upper gastrointestinal barium X-ray radiography. METHODS: A total of 6520 gastric X-ray images obtained from 815 subjects were analyzed. We designed a deep convolutional neural network (DCNN)-based gastritis detection scheme and evaluated the effectiveness of our method. The detection performance of our method was compared with that of ABC (D) stratification. RESULTS: Sensitivity, specificity, and harmonic mean of sensitivity and specificity of our method were 0.962, 0.983, and 0.972, respectively, and those of ABC (D) stratification were 0.925, 0.998, and 0.960, respectively. Although there were 18 false negative cases in ABC (D) stratification, 14 of those 18 cases were correctly classified into the positive group by our method. CONCLUSIONS: Deep learning techniques may be effective for evaluation of gastritis/non-gastritis. Collaborative use of DCNN-based gastritis detection systems and ABC (D) stratification will provide more reliable gastric cancer risk information.

リンク情報
DOI
https://doi.org/10.1007/s00535-018-1514-7
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/30284046

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