Artificial intelligence (AI) has revolutionized the medical diagnostic process of various diseases. Since the manual reading of capsule endoscopy videos is a time-intensive, error-prone process, computerized algorithms have been introduced to automate this process. Over the past decade, the evolution of convolutional neural network (CNN) enabled AI to detect multiple lesions simultaneously with increasing accuracy and sensitivity. Difficulty in validating CNN performance and unique characteristics of capsule endoscopy images make computer-aided reading systems in capsule endoscopy still on a preclinical level. Although AI technology can be used as an auxiliary second observer in capsule endoscopy, it is expected that in the near future, it will effectively reduce the reading time and ultimately become an independent, integrated reading system.

The role of gut microbiome-intestinal immune complex in the development of colorectal cancer and its progression is well recognized. Accordingly, certain microbial strains tend to colonize or vanish in patients with colorectal cancer. Probiotics, prebiotics, and synbiotics are expected to exhibit both anti-tumor effects and chemopreventive effects during cancer treatment through mechanisms such as xenometabolism, immune interactions, and altered eco-community. Microbial modulation can also be safely used to prevent complications during peri-operational periods of colorectal surgery. A deeper understanding of the role of intestinal microbiota as a target for colorectal cancer treatment will lead the way to a better prognosis for colorectal cancer patients.

With an aging population, the number of patients with difficulty in swallowing due to medical conditions is gradually increasing. In such cases, enteral nutrition is administered through a temporary nasogastric tube. However, the long-term use of a nasogastric tube leads to various complications and a decreased quality of life. Percutaneous endoscopic gastrostomy (PEG) is the percutaneous placement of a tube into the stomach that is aided endoscopically and may be an alternative to a nasogastric tube when enteral nutritional is required for four weeks or more. This paper is the first Korean clinical guideline for PEG developed jointly by the Korean College of Helicobacter and Upper Gastrointestinal Research and led by the Korean Society of Gastrointestinal Endoscopy. These guidelines aimed to provide physicians, including endoscopists, with the indications, use of prophylactic antibiotics, timing of enteric nutrition, tube placement methods, complications, replacement, and tube removal for PEG based on the currently available clinical evidence.

Artificial intelligence (AI) is a subfield of computer science that aims to implement computer systems that perform tasks that generally require human learning, reasoning, and perceptual abilities. AI is widely used in the medical field. The interpretation of medical images requires considerable effort, time, and skill. AI-aided interpretations, such as automated abnormal lesion detection and image classification, are promising areas of AI. However, when images with different characteristics are extracted, depending on the manufacturer and imaging environment, a so-called domain shift problem occurs in which the developed AI has a poor versatility. Domain adaptation is used to address this problem. Domain adaptation is a tool that generates a newly converted image which is suitable for other domains. It has also shown promise in reducing the differences in appearance among the images collected from different devices. Domain adaptation is expected to improve the reading accuracy of AI for heterogeneous image distributions in gastrointestinal (GI) endoscopy and medical image analyses. In this paper, we review the history and basic characteristics of domain shift and domain adaptation. We also address their use in gastrointestinal endoscopy and the medical field more generally through published examples, perspectives, and future directions.

Chung Hyun Tae, Ju Yup Lee, Moon Kyung Joo, Chan Hyuk Park, Eun Jeong Gong, Cheol Min Shin, Hyun Lim, Hyuk Soon Choi, Miyoung Choi, Sang Hoon Kim, Chul-Hyun Lim, Jeong-Sik Byeon, Ki-Nam Shim, Geun Am Song, Moon Sung Lee, Jong-Jae Park, Oh Young Lee, The Korean Society of Gastrointestinal Endoscopy Task Force on Clinical Practice Guidelines. Gut and Liver 2024;18:10-26. https://doi.org/10.5009/gnl230146

Sang Hoon Kim, Yura Choi, Jihong Oh, Eui Yeon Lim, Jung Eun Lee, Eun-Ji Song, Young-Do Nam, Hojun Kim. Gut and Liver 2024;18:621-31. https://doi.org/10.5009/gnl230130