@Article{Guimaraes2019,
  author       = {Pedro Guimaraes and Andreas Keller and Tobias Fehlmann and Frank Lammert and Markus Casper},
  title        = {Deep-learning based detection of gastric precancerous conditions},
  journal      = {Gut},
  publisher    = {BMJ Publishing Group},
  year         = {2019},
  pages        = {0017-5749},
  issn         = {0017-5749},
  issn-linking = {0017-5749},
  month        = aug,
  abstract     = {Conventional white-light endoscopy has high interobserver variability for the diagnosis of gastric precancerous conditions. Here we present a deep-learning (DL) approach for the diagnosis of atrophic gastritis developed and trained using real-world endoscopic images from the proximal stomach. The model achieved an accuracy of 93% (area under the curve (AUC): 0.98; F-score 0.93) in an independent data set, outperforming expert endoscopists. DL may overcome conventional appraisal of white-light endoscopy and support human decision making. The algorithm is available free of charge via a web-based interface (https://www.ccb.uni-saarland.de/atrophy).},
  doi          = {10.1136/gutjnl-2019-319347},
  pii          = {10.1136/gutjnl-2019-319347},
}