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CONCLUSION This study showed that CE on the visual sustained attention task seems to influence suicidal ideation as a result of interaction with depressive symptoms.Although surgery was the standard treatment for early gastrointestinal cancers, endoscopic resection is now a standard treatment for early gastrointestinal cancers without regional lymph node metastasis. High-definition white light endoscopy, chromoendoscopy, and image-enhanced endoscopy such as narrow band imaging are performed to assess the edge and depth of early gastrointestinal cancers for delineation of resection boundaries and prediction of the possibility of lymph node metastasis before the decision of endoscopic resection. Endoscopic mucosal resection and/or endoscopic submucosal dissection can be performed to remove early gastrointestinal cancers completely by en bloc fashion. Histopathological evaluation should be carefully made to investigate the presence of risk factors for lymph node metastasis such as depth of cancer invasion and lymphovascular invasion. Additional treatment such as radical surgery with regional lymphadenectomy should be considered if the endoscopically resected specimen shows risk factors for lymph node metastasis. This is the first Korean clinical practice guideline for endoscopic resection of early gastrointestinal cancer. GSK3685032 This guideline was developed by using mainly de novo methods and encompasses endoscopic management of superficial esophageal squamous cell carcinoma, early gastric cancer, and early colorectal cancer. This guideline will be revised as new data on early gastrointestinal cancer are collected.Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully.Diagnosis and evaluation of early gastric cancer (EGC) using endoscopic images is significantly important; however, it has some limitations. In several studies, the application of convolutional neural network (CNN) greatly enhanced the effectiveness of endoscopy. To maximize clinical usefulness, it is important to determine the optimal method of applying CNN for each organ and disease. Lesion�-based CNN is a type of deep learning model designed to learn the entire lesion from endoscopic images. This review describes the application of lesion-based CNN technology in diagnosis of EGC.Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology.BACKGROUND/AIMS We retrospectively analyzed Crohn's disease (CD) patients with small intestinal strictures who underwent single-balloon enteroscopy (SBE) to ascertain whether prototype SBEs with a passive bending mechanism and high force transmission insertion tube had better insertability in the small intestine than a conventional SBE. METHODS Among 253 CD patients who underwent SBE, we identified 94 CD patients who had undergone attempted endoscopic balloon dilatation (EBD) for small intestinal stenosis for inclusion in this study. We analyzed whether the type of scope used for their initial procedure affected the cumulative surgery-free rate. For the insertability analysis, patients who underwent SBE at least twice were divided into 3 groups according to the type of scope used conventional SBE only, prototype SBE only, and both conventional and prototype SBEs. For each group, depth of insertion, procedure time, and number of EBDs were compared in the same patient at different time points. RESULTS The success rate of EBD was 88.3%. The 5- and 10-year cumulative surgery-free rate was 75.7% and 72.8%, respectively. Cox regression analysis indicated that the factors contributing to surgery were long stricture (≥2 cm), EBD failure, and elevated Crohn's Disease Activity Index, but not the type of scope used for EBD. The prototype SBEs significantly improved the depth of insertion (P=0.03, Wilcoxon's signed-rank test). CONCLUSIONS In CD patients with small intestinal stenosis, the prototype SBEs with a passive bending mechanism and high force transmission insertion tube did not improve long-term EBD outcome but did improve deep insertability. (Clinical Trial Registration No. UMIN000037102).