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Gastroenterologists

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Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Cancer invasion depth is a critical factor affecting the choice of treatment in patients with superficial squamous cell carcinoma (SCC). However, the diagnosis of invasion depth is currently subjective and liable to interobserver...

Gastroenterologist-Level Identification of Small-Bowel DiseasesĀ and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model.

Gastroenterology
BACKGROUND & AIMS: Capsule endoscopy has revolutionized investigation of the small bowel. However, this technique produces a video that is 8-10 hours long, so analysis is time consuming for gastroenterologists. Deep convolutional neural networks (CNN...

Early Esophageal Cancer: A Gastroenterologist's Disease.

Digestive diseases and sciences
Traditionally, early esophageal cancer (i.e., cancer limited to the mucosa or superficial submucosa) was managed surgically; the gastroenterologist's role was primarily to diagnose the tumor. Over the last decade, advances in endoscopic imaging, abla...

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.

World journal of gastroenterology
Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) d...

Artificial intelligence compared with human-derived patient educational materials on cirrhosis.

Hepatology communications
BACKGROUND: The study compared the readability, grade level, understandability, actionability, and accuracy of standard patient educational material against artificial intelligence chatbot-derived patient educational material regarding cirrhosis.