Table 2.

Reported applications of computer-aided diagnosis and artificial intelligence in various endoscopic procedures

Procedure	Application
Colonoscopy	Detection of polyps (real time and on still images and video)∗
	Classification of polyps (neoplastic vs hyperplastic)∗
	Detection of malignancy within polyps (depth of invasion on endocytoscopic images)∗
	Presence of inflammation on endocytoscopic images∗
Wireless capsule endoscopy (WCE)	Lesion detection and classification (bleeding, ulcers, polyps)∗
	Assessment of intestinal motility
	Celiac disease (assessment of villous atrophy, intestinal motility)
	Improve efficiency of image review
	Deletion of duplicate images and uninformative image frames (eg, images with debris)∗
Upper endoscopy	Identify anatomical location∗
	Diagnosis of Helicobacter pylori infection status∗
	Gastric cancer detection and assessing depth of invasion∗
	Esophageal squamous dysplasia
	Detection and delineation of early dysplasia in Barrett’s esophagus∗
	Real-time image segmentation in volumetric laser endomicroscopy (VLE) in Barrett’s esophagus∗
Endoscopic ultrasound (EUS)	Differentiation of pancreatic cancer from chronic pancreatitis and normal pancreas
	Differentiation of autoimmune pancreatitis from chronic pancreatitis
	EUS elastography

^∗Applications in which use of deep learning has been reported.