Feedback from artificial intelligence improved the learning of junior endoscopists on histology prediction of gastric lesions.

Artificial intelligence (AI)-assisted image classification has been shown to have high accuracy on endoscopic diagnosis. We evaluated the potential effects of use of an AI-assisted image classifier on training of junior endoscopists for histological prediction of gastric lesions. An AI image classifier was built on a convolutional neural network with five convolutional layers and three fully connected layers A Resnet backbone was trained by 2,000 non-magnified endoscopic gastric images.

Linked color imaging versus narrow-band imaging for colorectal polyp detection: a prospective randomized tandem colonoscopy study.

Background And Aims: Linked color imaging (LCI) is a newly available image-enhanced endoscopy (IEE) system that emphasizes the red mucosal color. No study has yet compared LCI with other available IEE systems. Our aim was to investigate polyp detection rates using LCI compared with narrow-band imaging (NBI).