Background Further development of deep learning-based artificial intelligence (AI)
technology to automatically diagnose multiple abnormalities in small-bowel capsule …

Background & Aims Capsule endoscopy has revolutionized investigation of the small bowel.
However, this technique produces a video that is 8–10 hours long, so analysis is time …

Methods: We demonstrated the use of deep learning models in CE image analysis,
specifically by piloting a bowel preparation model (BPM) and an abnormality detection …

Background and Aims A deep convolutional neural network (CNN) system could be a high-
level screening tool for capsule endoscopy (CE) reading but has not been established for …

Background Although great advances in artificial intelligence for interpreting small bowel
capsule endoscopy (SBCE) images have been made in recent years, its practical use is still …

Abstract Background and Aim Convolutional neural network (CNN) systems that
automatically detect abnormalities from small‐bowel capsule endoscopy (SBCE) images are …

The manual reading of capsule endoscopy (CE) videos in small bowel disease diagnosis is
time-intensive. Algorithms introduced to automate this process are premature for real clinical …

Objectives Although several studies have shown the usefulness of artificial intelligence to
identify abnormalities in small‐bowel capsule endoscopy (SBCE) images, few studies have …

Objectives Artificial intelligence (AI) may be practical for image classification of small bowel
capsule endoscopy (CE). However, creating a functional AI model is challenging. We …

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 …