Background Artificial intelligence (AI) has recently been applied to endoscopy and
questionnaires for the evaluation of oesophageal diseases (ODs). Aim We performed a …

Upper gastrointestinal (GI) neoplasia account for 35% of GI cancers and 1.5 million cancer-
related deaths every year. Despite its efficacy in preventing cancer mortality, diagnostic …

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 …

Background and Aims The visual detection of early esophageal neoplasia (high-grade
dysplasia and T1 cancer) in Barrett's esophagus (BE) with white-light and virtual …

Based on previous work by our group with manual annotation of visible Barrett oesophagus
(BE) cancer images, a real-time deep learning artificial intelligence (AI) system was …

Background and Aims Few artificial intelligence-based technologies have been developed
to improve the efficiency of screening for esophageal squamous cell carcinoma (ESCC) …

Simple Summary Detection of early esophageal cancer is important to improve patient
survival, however, early diagnosis of the cancer cells is difficult, even for experienced …

Objective Artificial intelligence (AI) may reduce underdiagnosed or overlooked upper GI
(UGI) neoplastic and preneoplastic conditions, due to subtle appearance and low disease …

ABSTRACT Background and Aims Diagnosis of esophageal cancer or precursor lesions by
endoscopic imaging depends on endoscopist expertise and is inevitably subject to …

Conventional white-light endoscopy has high interobserver variability for the diagnosis of
gastric precancerous conditions. Here we present a deeplearning (DL) approach for the …