- White-light endoscopy (WLE) is the most pivotal tool to detect gastric cancer in an early stage. However, the skill among endoscopists varies greatly. Here, we aim to develop a deep learning–based system named ENDOANGEL-LD (lesion detection) to assist in detecting all focal gastric lesions and predicting neoplasms by WLE.
- Endoscopic differential diagnoses of gastric mucosal lesions (benign gastric ulcer, early gastric cancer [EGC], and advanced gastric cancer) remain challenging. We aimed to develop and validate convolutional neural network–based artificial intelligence (AI) models: lesion detection, differential diagnosis (AI-DDx), and invasion depth (AI-ID; pT1a vs pT1b among EGC) models.
- We aimed to develop and validate a deep learning–based system that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting gastric neoplasm, identifying EGC, and predicting EGC invasion depth and differentiation status. Herein, we provide a state-of-the-art comparison of the system with endoscopists using real-time videos in a nationwide human–machine competition.
- Gastric cancer remains a major cause of morbidity and mortality worldwide.1 Identifying early gastric cancer (EGC) during endoscopy is crucial because of the prognostic consequences associated with early diagnosis. Despite considerable technical developments in endoscopic practice, including magnified endoscopy (ME) with narrow-band imaging (NBI), the gastric cancer missed rate remains as high as 10%.2 Furthermore, considerable interobserver differences in the characterization of lesions identified during gastroscopy have been reported.
- Narrow-band imaging with magnifying endoscopy (ME-NBI) has shown advantages in the diagnosis of early gastric cancer (EGC). However, proficiency in diagnostic algorithms requires substantial expertise and experience. In this study, we aimed to develop a computer-aided diagnostic model for EGM (EGCM) to analyze and assist in the diagnosis of EGC under ME-NBI.
- Artificial intelligence (AI) for GI endoscopy is an important and rapidly growing area of research. Much initial work in AI for endoscopy has focused on detection and optical diagnosis of colon polyps. However, AI has the potential to aid clinical decision making in many other aspects of gastroenterology.1 In this issue of Gastrointestinal Endoscopy, Zhou and colleagues2 explore the potential of AI to address one of the most clinically important issues in the management of early gastric cancers (EGCs): prediction of invasion depth.