Background and Aims The quality of EGD is a prerequisite for a high detection rate of upper
GI lesions, especially early gastric cancer. Our previous study showed that an artificial …

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

Background Early gastric cancer (EGC) has a high survival rate, but it is difficult to diagnosis.
Recently, artificial intelligence (AI) based on deep convolutional neural network (DCNN) has …

Objectives Detecting early gastric cancer is difficult, and it may even be overlooked by
experienced endoscopists. Recently, artificial intelligence based on deep learning through …

Background We aimed to develop and validate a real-time deep convolutional neural
networks (DCNNs) system for detecting early gastric cancer (EGC). Methods All 45,240 …

Background Gastric cancer is the third most lethal malignancy worldwide. A novel deep
convolution neural network (DCNN) to perform visual tasks has been recently developed …

Background Diagnosing early gastric cancer based on endoscopic findings requires
training, is time-consuming and difficult, and may produce false-negative results depending …

Background Upper gastrointestinal cancers (including oesophageal cancer and gastric
cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep …

Background Observation of the entire stomach during esophagogastroduodenoscopy (EGD)
is important; however, there is a lack of effective evaluation tools. Aims To develop an …

Background Diagnosis of early gastric cancer (EGC) under narrow band imaging endoscopy
(NBI) is dependent on expertise and skills. We aimed to elucidate whether artificial …