Background In a previous study, we examined the use of deep learning models to classify
the invasion depth (mucosa-confined versus submucosa-invaded) of gastric neoplasms …

Background Deep learning models have previously been established to predict the
histopathology and invasion depth of gastric lesions using endoscopic images. This study …

Endoscopic resection is recommended for gastric neoplasms confined to mucosa or
superficial submucosa. The determination of invasion depth is based on gross morphology …

Background and Aims We aimed to develop and validate a deep learning–based system
that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting …

Background and Aims Endoscopic differential diagnoses of gastric mucosal lesions (benign
gastric ulcer, early gastric cancer [EGC], and advanced gastric cancer) remain challenging …

Background Gastric cancer is a common kind of malignancies, with yearly occurrences
exceeding one million worldwide in 2017. Typically, ulcerous and cancerous tissues …

Background Artificial intelligence (AI) for gastric cancer diagnosis has been discussed in
recent years. The role of AI in early gastric cancer is more important than in advanced gastric …

Background and Aims Prediction of intramucosal gastric cancer (GC) is a big challenge. It is
not clear whether artificial intelligence could assist endoscopists in the diagnosis. Methods A …

Background Machine learning has led to several endoscopic studies about the automated
localization of digestive lesions and prediction of cancer invasion depth. Training and …

Simple Summary Gastric cancer (GC) is one of the most newly diagnosed cancers and the
fifth leading cause of death globally. Previous studies reported that the detection rate of …