Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow‐band imaging

Background and Aim Magnifying endoscopy with narrow‐band imaging (ME‐NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME‐NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep l...

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Veröffentlicht in:	Journal of gastroenterology and hepatology Jg. 36; H. 2; S. 482 - 489
Hauptverfasser:	Ueyama, Hiroya, Kato, Yusuke, Akazawa, Yoichi, Yatagai, Noboru, Komori, Hiroyuki, Takeda, Tsutomu, Matsumoto, Kohei, Ueda, Kumiko, Matsumoto, Kenshi, Hojo, Mariko, Yao, Takashi, Nagahara, Akihito, Tada, Tomohiro
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Australia Wiley Subscription Services, Inc 01.02.2021 John Wiley and Sons Inc
Schlagworte:	Accuracy Artificial intelligence convolutional neural network Deep learning Diagnosis early gastric cancer Endoscopy Gastric cancer Gastritis Intestine magnifying endoscopy Mucosa narrow‐band imaging Neural networks Regular magnifying endoscopy convolutional neural network artificial intelligence narrow-band imaging early gastric cancer
ISSN:	0815-9319, 1440-1746, 1440-1746
Online-Zugang:	Volltext
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