2018
DOI: 10.1371/journal.pone.0193197
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Classification of atrophic mucosal patterns on Blue LASER Imaging for endoscopic diagnosis of Helicobacter pylori-related gastritis: A retrospective, observational study

Abstract: BackgroundAtrophic gastritis can be classified according to characteristic mucosal patterns observed by Blue LASER Imaging (BLI) in a medium-range to distant view.AimsTo facilitate the endoscopic diagnosis of Helicobacter pylori (HP)-related gastritis, we investigated whether atrophic mucosal patterns correlated with HP infection based on the image interpretations of three endoscopists blinded to clinical features.MethodsThis study included 441 patients diagnosed as having atrophic gastritis by upper gastroint… Show more

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Cited by 23 publications
(18 citation statements)
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“…BLI‐bright is a brighter BLI to illuminate a wide lumen. BLI‐bright retrospectively identified that spotty pattern of atrophic mucosa correlated with active H. pylori infection, the cracked pattern of atrophic mucosa correlated with post‐inflammatory change after H. pylori eradication, and the mottled pattern was related with intestinal metaplasia …”
Section: Iee For Diagnosis Of Kyoto Classification Of Gastritismentioning
confidence: 99%
“…BLI‐bright is a brighter BLI to illuminate a wide lumen. BLI‐bright retrospectively identified that spotty pattern of atrophic mucosa correlated with active H. pylori infection, the cracked pattern of atrophic mucosa correlated with post‐inflammatory change after H. pylori eradication, and the mottled pattern was related with intestinal metaplasia …”
Section: Iee For Diagnosis Of Kyoto Classification Of Gastritismentioning
confidence: 99%
“…They defined three types of mucosa: spotty, cracked, and mottled. The first appears to represent H. pylori infection, the second a postinflammatory state after H. pylori eradication, and the last intestinal metaplasia, with a good predictive value …”
Section: Endoscopic Diagnosismentioning
confidence: 99%
“…The first appears to represent H. pylori infection, the second a postinflammatory state after H. pylori eradication, and the last intestinal metaplasia, with a good predictive value. 1 In another study, Itoh et al developed a convolutional neural network which is a machine-learning algorithm capable of recognizing specific features of gastric endoscopy images to detect H. pylori infection earlier. Both the sensitivity and specificity for detection of H. pylori infection were 86.7%.…”
Section: Endoscopi C D Iag Nos Ismentioning
confidence: 99%
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