A highly sensitive and highly specific convolutional neural network-basedalgorithm for automated diagnosis of angiodysplasia in small bowel capsule endoscopy

Capsule endoscopy (CE) has become a standard non-invasive tool for small bowel (SB) examination. However, with an average number of 50,000 SB still frames per CE video, lesions can be missed and CE reading remains a timeconsuming activity. Therefore, the development of computer-aided algorithms for lesions’ detection has become an active research area in CE. Gastro-intestinal angiodysplasias (AGD) are the most common SB vascular lesions with an inherent risk of bleeding. This study aimed to develop a computer-assisted diagnosis (CAD) tool for SB-AGD detection in CE.