With the advances in endoscopic technologies and artificial intelligence, a large number of
endoscopic imaging datasets have been made public to researchers around the world. This …

Background and Aims Publicly available databases containing colonoscopic imaging data
are valuable resources for artificial intelligence (AI) research. Currently, little is known …

Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large
number of highly lethal GI cancers. Early GI cancer precursors are often missed during the …

Background and Aims Artificial intelligence (AI)-based applications have transformed
several industries and are widely used in various consumer products and services. In …

Background Artificial intelligence (AI) has made considerable progress in image recognition,
especially in the analysis of endoscopic images. The availability of large‐scale annotated …

Purpose Upper gastrointestinal (GI) endoscopic image documentation has provided an
efficient, low-cost solution to address quality control for endoscopic reporting. The problem …

Purpose As with several medical image analysis tasks based on deep learning,
gastrointestinal image analysis is plagued with data scarcity, privacy concerns and an …

Over the past decade, technological advances in deep learning have led to the introduction
of artificial intelligence (AI) in medical imaging. The most commonly used structure in image …

Virtual colonoscopy (VC) allows a physician to virtually navigate within a reconstructed 3D
colon model searching for colorectal polyps. Though VC is widely recognized as a highly …

Objectives Artificial intelligence (AI) may be practical for image classification of small bowel
capsule endoscopy (CE). However, creating a functional AI model is challenging. We …