Abstract
The gold standard and most widely used approach for screening and surveillance of Barrett’s esophagus (BE) is esophagogastroduodenoscopy. However, the visual detection of early esophageal neoplasia (high grade dysplasia and T1 stage adenocarcinoma) in BE with white light and virtual chromoendoscopy is still often difficult. We previously designed a convolutional neural artificial intelligence algorithm to detect and localize dysplasia seen on still images. The aim of this study is to assess if this system can detect early esophageal neoplasia in BE in endoscopic video.
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