Background & Aims We aimed to develop and validate a deep-learning computer-aided
detection (CAD) system, suitable for use in real time in clinical practice, to improve endoscopic …

Background Computer-aided detection (CADe) systems could assist endoscopists in detecting
early neoplasia in Barrett's oesophagus, which could be difficult to detect in endoscopic …

Introduction Endoscopic detection of early neoplasia in Barrett's esophagus is difficult.
Computer Aided Detection (CADe) systems may assist in neoplasia detection. The aim of this …

Background and Aims We assessed the preliminary diagnostic accuracy of a recently developed
computer-aided detection (CAD) system for detection of Barrett’s neoplasia during live …

There has been a vast increase in GI literature focused on the use of machine learning in
endoscopy. The relative novelty of this field poses a challenge for reviewers and readers of GI …

Background and aims Early gastric cancer (EGC) lesions are often subtle and endoscopically
poorly visible. The aim of this study is to evaluate the additive effect of linked color imaging …

Background and Aims Endoscopic recognition of early Barrett’s neoplasia is challenging.
Blue-light imaging (BLI) and linked-color imaging (LCI) may assist endoscopists in …

Method The CADe system was developed using a large and heterogeneous BE training
dataset originating from 15 international endoscopy centers, including 6.237 neoplastic (1.304 …

Patients suffering from Barrett's Esophagus (BE) are at an increased risk of developing
esophageal adenocarcinoma and early detection is crucial for a good prognosis. To aid the …

Gastrointestinal endoscopic image analysis presents significant challenges, such as
considerable variations in quality due to the challenging in-body imaging environment, the often-…