Objective Employing transfer learning (TL) with convolutional neural networks (CNNs), well-
trained on non-medical ImageNet dataset, has shown promising results for medical image …

The development of artificial intelligence (AI) has increased dramatically in the last 20 years,
with clinical applications progressively being explored for most of the medical specialties …

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 …

Background Esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper
gastrointestinal lesions especially early gastric cancer (EGC). An artificial intelligence …

Abstract Background and Aim Magnifying endoscopy with narrow‐band imaging (ME‐NBI)
has made a huge contribution to clinical practice. However, acquiring skill at ME‐NBI …

Objective This study aims to systematically review the application of artificial intelligence (AI)
techniques in gastric cancer and to discuss the potential limitations and future directions of …

Background Early detection of early gastric cancer (EGC) allows for less invasive cancer
treatment. However, differentiating EGC from gastritis remains challenging. Although …

Gastric cancer is the fourth leading cause of cancer-related mortality across the globe, with a
5-year survival rate of less than 40%. In recent years, several applications of artificial …

Deep learning has achieved remarkably positive results and impacts on medical diagnostics
in recent years. Due to its use in several proposals, deep learning has reached sufficient …

Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an
increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular …