METHODS: A deep learning algorithm based on a convolutional neural network (CNN) was
designed. Each frame was evaluated for the presence of enteric protruding lesions. The …

Results: a total of 18,625 CE images (2,830 showing protruding lesions and 15,795 normal
mucosa) were included. Training and validation datasets were built with an 80%/20 …

Background and Objectives: Device-assisted enteroscopy (DAE) has a significant role in
approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and …

Background This study was aimed to develop a computer-aided diagnosis (CAD) system
with deep-learning technique and to validate its efficiency on detecting the four categories of …

Simple Summary Device-assisted enteroscopy is the only diagnostic and therapeutic exam
capable of exploring the entire gastrointestinal tract. However, the diagnostic yield of this …

Background and Aims Recent advances in deep convolutional neural networks (CNNs)
have led to remarkable results in digestive endoscopy. In this study, we aimed to develop …

Background: Colon capsule endoscopy (CCE) is an alternative for patients unwilling or with
contraindications for conventional colonoscopy. Colorectal cancer screening may benefit …

Background Colon capsule endoscopy (CCE) is a minimally invasive alternative for patients
unwilling to undergo conventional colonoscopy, or for whom the latter exam is …

Background and Aims Protruding lesions of the small bowel vary in wireless capsule
endoscopy (WCE) images, and their automatic detection may be difficult. We aimed to …

Background Previous computer-aided detection systems for diagnosing lesions in images
from wireless capsule endoscopy (WCE) have been limited to a single type of small-bowel …