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Gastrointestinal cancers are characterized by high incidence and mortality. However, there are well-established methods of screening. The endoscopy exam provides the macroscopical image and enables harvesting the tissue samples for further histopathological diagnosis. The efficiency of endoscopies relies not only on proper patient preparation, but also on the skills of the personnel conducting the exam. In recent years, a number of reports concerning the application of artificial intelligence (AI) in medicine have arisen. Numerous studies aimed to assess the utility of deep learning/ neural network systems supporting endoscopies. In this review, we summarized the most recent reports and randomized clinical trials regarding the application of AI in screening and surveillance of gastrointestinal cancers among patients suffering from esophageal, gastric, and colorectal cancer, along with the advantages, limitations, and controversies of those novel solutions. [ABSTRACT FROM AUTHOR]

Our review delves into the realm of peroral endoscopic myotomies (POEMs) in the upper gastrointestinal tract (UGT). In recent years, POEMs have brought about a revolution in the treatment of UGT motility disorders. Esophageal POEM, the first to be introduced, has now been validated as the primary treatment for achalasia. Subsequently developed, G-POEM displays promising results in addressing refractory gastroparesis. Over time, multiple endoscopic myotomy techniques have emerged for the treatment of Zenker's diverticulum, including Z-POEM, POES, and hybrid approaches. Despite the well-established efficacy outcomes, new challenges arise in the realm of POEMs in the UGT. For esophageal POEM, the future scenario lies in customizing the myotomy extent to the minimum necessary, while for G-POEM, it involves identifying patients who can optimally benefit from the treatment. For ZD, it is crucial to validate an algorithm that considers various myotomy options according to the diverticulum's size and in relation to individual patients. These challenges align with the concept of precision endoscopy, personalizing the technique for each subject. Within our text, we comprehensively examine each myotomy technique, analyzing indications, outcomes, and adverse events. Additionally, we explore the emerging challenges posed by myotomies within the context of the evolving field of precision endoscopy. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has the potential to assist in endoscopy and improve decision making, particularly in situations where humans may make inconsistent judgments. The performance assessment of the medical devices operating in this context is a complex combination of bench tests, randomized controlled trials, and studies on the interaction between physicians and AI. We review the scientific evidence published about GI Genius, the first AI-powered medical device for colonoscopy to enter the market, and the device that is most widely tested by the scientific community. We provide an overview of its technical architecture, AI training and testing strategies, and regulatory path. In addition, we discuss the strengths and limitations of the current platform and its potential impact on clinical practice. The details of the algorithm architecture and the data that were used to train the AI device have been disclosed to the scientific community in the pursuit of a transparent AI. Overall, the first AI-enabled medical device for real-time video analysis represents a significant advancement in the use of AI for endoscopies and has the potential to improve the accuracy and efficiency of colonoscopy procedures. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention. [ABSTRACT FROM AUTHOR]

Eosinophilic esophagitis is a Th-2 antigen-mediated disease in which there is an influx of eosinophils to all layers of the esophagus, triggering an inflammatory response. Chronic inflammatory process causes esophageal remodeling, leading to difficulties in swallowing. Food impaction, heartburn, and chest pain are other characteristic (but not pathognomonic) symptoms in adults. Although the disease has only been described since in the early 1970s, its incidence and prevalence are rapidly growing, especially in Western countries. According to the diagnostic guidelines, there should be at least 15 eosinophils visible per high-power field in biopsies obtained from different sites in the esophagus upon endoscopy with relevant esophageal symptoms. Other diseases that can cause esophageal eosinophilia should be ruled out. Eosinophilic esophagitis treatment may be challenging; however, new methods of management have recently emerged. The currently used proton pump inhibitors, topical corticosteroids, and elimination diet are combined with biological treatment. New methods for disease diagnostics and clinical course assessment are also available. This review presents current knowledge about the disease, supported by the latest research data. [ABSTRACT FROM AUTHOR]

Submucosal endoscopy (third-space endoscopy) can be defined as an endoscopic procedure performed in the submucosal space. This procedure is novel and has been utilized for delivery to the submucosal space in a variety of gastrointestinal diseases, such as a tumor, achalasia, gastroparesis, and subepithelial tumors. The main submucosal endoscopy includes peroral endoscopic myotomy, gastric peroral endoscopic myotomy, Zenker peroral endoscopic myotomy, submucosal tunneling for endoscopic resection, and endoscopic submucosal tunnel dissection. Submucosal endoscopy has been used as a viable alternative to surgical techniques because it is minimally invasive in the treatment and diagnosis of gastrointestinal diseases and disorders. However, there is limited evidence to prove this. This article reviews the current applications and evidence regarding submucosal endoscopy while exploring the possible future clinical applications in this field. As our understanding of these procedures improves, the future of submucosal endoscopy could be promising in the fields of diagnostic and therapeutic endoscopy. [ABSTRACT FROM AUTHOR]

Artificial intelligence, a computer-based concept that tries to mimic human thinking, is slowly becoming part of the endoscopy lab. It has developed considerably since the first attempt at developing an automated medical diagnostic tool, today being adopted in almost all medical fields, digestive endoscopy included. The detection rate of preneoplastic lesions (i.e., polyps) during colonoscopy may be increased with artificial intelligence assistance. It has also proven useful in detecting signs of ulcerative colitis activity. In upper digestive endoscopy, deep learning models may prove to be useful in the diagnosis and management of upper digestive tract diseases, such as gastroesophageal reflux disease, Barrett's esophagus, and gastric cancer. As is the case with all new medical devices, there are challenges in the implementation in daily medical practice. The regulatory, economic, organizational culture, and language barriers between humans and machines are a few of them. Even so, many devices have been approved for use by their respective regulators. Future studies are currently striving to develop deep learning models that can replicate a growing amount of human brain activity. In conclusion, artificial intelligence may become an indispensable tool in digestive endoscopy. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has shown promising results in digestive endoscopy, especially in capsule endoscopy (CE). However, some physicians still have some difficulties and fear the advent of this technology. We aimed to evaluate the perceptions and current sentiments toward the use of AI in CE. An online survey questionnaire was sent to an audience of gastroenterologists. In addition, several European national leaders of the International CApsule endoscopy REsearch (I CARE) Group were asked to disseminate an online survey among their national communities of CE readers (CER). The survey included 32 questions regarding general information, perceptions of AI, and its use in daily life, medicine, endoscopy, and CE. Among 380 European gastroenterologists who answered this survey, 333 (88%) were CERs. The mean average time length of experience in CE reading was 9.9 years (0.5–22). A majority of CERs agreed that AI would positively impact CE, shorten CE reading time, and help standardize reporting in CE and characterize lesions seen in CE. Nevertheless, in the foreseeable future, a majority of CERs disagreed with the complete replacement all CE reading by AI. Most CERs believed in the high potential of AI for becoming a valuable tool for automated diagnosis and for shortening the reading time. Currently, the perception is that AI will not replace CE reading. [ABSTRACT FROM AUTHOR]