Exploring vision transformers for classifying early Barrett's dysplasia in endoscopic images: A pilot study on white-light and narrow-band imaging.

Background And Aim: Various deep learning models, based on convolutional neural network (CNN), have been shown to improve the detection of early esophageal neoplasia in Barrett's esophagus. Vision transformer (ViT), derived from natural language processing, has emerged as the new state-of-the-art for image recognition, outperforming predecessors such as CNN. This pilot study explores the use of ViT to classify the presence or absence of early esophageal neoplasia in endoscopic images of Barrett's esophagus.

Moving Forward: Gradual Return of Gastroenterology Practice during the COVID-19 Pandemic.

Background: The COVID-19 pandemic has caused disruption of routine gastroenterology practice, which has resulted in the suspension of elective endoscopic procedures and outpatient consults. For the past months, the strategy was to mitigate infection risk for the healthcare team while still providing essential service to patients. Prolonged suspension of the outpatient clinics and endoscopy practice, however, is deemed unsustainable and could even be detrimental.