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Chronic pancreatitis (CP) is historically defined as an irreversible inflammatory condition of the pancreas leading to varying degrees of exocrine and endocrine dysfunction. Recently however, the paradigm for the diagnosis has changed in that it breaks with the traditional clinicopathologic-based definition of disease, focusing instead on diagnosing the underlying pathologic process early in the disease course and managing the syndrome more holistically to change the natural course of disease and minimize adverse disease effects. Currently, the most accepted mechanistically derived definition of CP is a pathologic fibroinflammatory syndrome of the pancreas in individuals with genetic, environmental, and/or other risk factors who develop persistent pathologic responses to parenchymal injury or stress. The most common symptom of CP is abdominal pain, with other symptoms such as exocrine pancreatic insufficiency and diabetes developing at highly variable rates. CP is most commonly caused by toxins such as alcohol or tobacco use, genetic polymorphisms, and recurrent attacks of acute pancreatitis, although no history of acute pancreatitis is seen in many patients. Diagnosis is made usually on cross-sectional imaging, with modalities such as endoscopic ultrasonography and pancreatic function tests playing a secondary role. Total pancreatectomy represents the only known cure for CP, although difficulty in patient selection and the complications inherent to this intervention make it usually an unattractive option. This guideline will provide an evidence-based practical approach to the diagnosis and management of CP for the general gastroenterologist.

Background and Study Aims Success rate of cecal intubation, endoscopist's difficulty, and procedure-related patient pain are still problems for beginners performing colonoscopy. New methods to aid colonoscopic insertion such as warm water instillation and oil lubrication have been proposed. The aim of this study is to evaluate the feasibility of using warm water or oil in colonoscopy. Methods Colonoscopy was performed in 117 unsedated patients by three endoscopists-in-training. Patients were randomly allocated to three groups, using a conventional method with administration of antispasmodics, warm water instillation, and oil lubrication, respectively. Success rate of total intubation within time limit (15 min), cecal intubation time, degree of endoscopist's difficulty, and level of patient discomfort were compared among the three groups. Results Cecal intubation time was shorter in the warm water group than in the conventional and oil groups. Degree of procedural difficulty was lower in the warm water group, and patient pain score was higher in the oil lubrication group, compared with the other groups. However, there was no significant difference in success rate of intubation within time limit among the three groups. Conclusions The warm water method is a simple, safe, and feasible method for beginners. Oil lubrication may not be a useful method compared with conventional and warm water method.

Enhanced characterization of esophageal peristaltic and sphincter function provided by esophageal pressure topography (EPT) offers a potential diagnostic advantage over conventional line tracings (CLT). However, high-resolution manometry (HRM) and EPT require increased equipment costs over conventional systems and evidence demonstrating a significant diagnostic advantage of EPT over CLT is limited. Our aim was to investigate whether the inter-rater agreement and/or accuracy of esophageal motility diagnosis differed between EPT and CLT. Forty previously completed patient HRM studies were selected for analysis using a customized software program developed to perform blinded independent interpretation in either EPT or CLT (six pressure sensors) format. Six experienced gastroenterologists with a clinical focus in esophageal disease (attendings) and six gastroenterology trainees with minimal manometry experience (fellows) from three academic centers interpreted each of the 40 studies using both EPT and CLT formats. Rater diagnoses were assessed for inter-rater agreement and diagnostic accuracy, both for exact diagnosis and for correct identification of a major esophageal motility disorder. The total group agreement was moderate (κ=0.57; 95% CI: 0.56-0.59) for EPT and fair (κ=0.32; 0.30-0.33) for CLT. Inter-rater agreement between attendings was good (κ=0.68; 0.65-0.71) for EPT and moderate (κ=0.46; 0.43-0.50) for CLT. Inter-rater agreement between fellows was moderate (κ=0.48; 0.45-0.50) for EPT and poor to fair (κ=0.20; 0.17-0.24) for CLT. Among all raters, the odds of an incorrect exact esophageal motility diagnosis were 3.3 times higher with CLT assessment than with EPT (OR: 3.3; 95% CI: 2.4-4.5; P<0.0001), and the odds of incorrect identification of a major motility disorder were 3.4 times higher with CLT than with EPT (OR: 3.4; 2.4-5.0; P<0.0001). Superior inter-rater agreement and diagnostic accuracy of esophageal motility diagnoses were demonstrated with analysis using EPT over CLT among our selected raters. On the basis of these findings, EPT may be the preferred assessment modality of esophageal motility.

We developed and validated a deep-learning algorithm for polyp detection. We used a YOLOv2 to develop the algorithm for automatic polyp detection on 8,075 images (503 polyps). We validated the algorithm using three datasets: A: 1,338 images with 1,349 polyps; B: an open, public CVC-clinic database with 612 polyp images; and C: 7 colonoscopy videos with 26 polyps. To reduce the number of false positives in the video analysis, median filtering was applied. We tested the algorithm performance using 15 unaltered colonoscopy videos (dataset D). For datasets A and B, the per-image polyp detection sensitivity was 96.7% and 90.2%, respectively. For video study (dataset C), the per-image polyp detection sensitivity was 87.7%. False positive rates were 12.5% without a median filter and 6.3% with a median filter with a window size of 13. For dataset D, the sensitivity and false positive rate were 89.3% and 8.3%, respectively. The algorithm detected all 38 polyps that the endoscopists detected and 7 additional polyps. The operation speed was 67.16 frames per second. The automatic polyp detection algorithm exhibited good performance, as evidenced by the high detection sensitivity and rapid processing. Our algorithm may help endoscopists improve polyp detection.

Previous studies have identified an increasing number of gastroenterology (GI) procedures using anesthesia services to provide sedation, with a majority of these services delivered to low-risk patients. The aim of this study was to update these trends with the most recent years of data. We used Medicare and commercial claims data from 2010 to 2013 to identify GI procedures and anesthesia services based on CPT codes, which were linked together using patient identifiers and dates of service. We defined low-risk patients as those who were classified as ASA (American Society of Anesthesiologists) physical status class I or II. For those patients without an ASA class listed on the claim, we used a prediction algorithm to impute an ASA physical status. Over 6.6 million patients in our sample had a GI procedure between 2010 and 2013. GI procedures involving anesthesia service accounted for 33.7% in 2010 and 47.6% in 2013 in Medicare patients, and 38.3% in 2010 and 53.0% in 2013 in commercially insured patients. Overall, as more patients used anesthesia services, total anesthesia service use in low-risk patients increased 14%, from 27,191 to 33,181 per million Medicare enrollees. Similarly, we observed a nearly identical uptick in commercially insured patients from 15,871 to 22,247 per million, an increase of almost 15%. During 2010-2013, spending associated with anesthesia services in low-risk patients increased from US$3.14 million to US$3.45 million per million Medicare enrollees and from US$7.69 million to US$10.66 million per million commercially insured patients. During 2010 to 2013, anesthesia service use in GI procedures continued to increase and the proportion of these services rendered for low-risk patients remained high.

The relevance of functional gastrointestinal (GI) disorders and their impact on quality of life for many patients has become an increasingly important topic in gastroenterology. A gastroenterologist can expect to see 40% of patients for motility and functional GI disorders, thus highlighting the necessity for physicians to have a strong foundation of knowledge in treatment strategies for these patients with complex disorders. A significant number of patients who suffer with functional GI disorders turn to complementary and alternative therapies to maintain control over their symptoms and often are happy with therapeutic results. This narrative presents information and treatment algorithms for the gastroenterologist to better understand and use some of the most common complementary and alternative therapies for patients with functional dyspepsia, nausea and vomiting, and irritable bowel syndrome.

The advances in artificial intelligence (AI) and machine learning (ML) technologies have created an explosion of research in AI-driven device development in gastroenterology. This is because machine learning can be reliably trained on and applied to diagnostic images captured during endoscopy. Recently, several randomized trials evaluating AI and ML for colon polyp detection have been published. Studies have been conducted evaluating the role of AI in dysplasia surveillance and other gastrointestinal (GI) disorders (1).These technologies pose unique regulatory challenges because there is no precedent for the US Food and Drug Administration (FDA) to approve and regulate software which continually evolve and adapt based on real-world data.In this article, we summarize the current concepts of the regulatory framework for medical software-assisted devices. We hope this would help the readers understand the processes involved before FDA approval for these devices.

Pancreatic exocrine insufficiency (PEI) is defined as a reduction in pancreatic exocrine secretion below the level that allows the normal digestion of nutrients. Pancreatic disease and surgery are the main causes of PEI. However, other conditions and upper gastrointestinal surgery can also affect the digestive function of the pancreas. PEI can cause symptoms of nutritional malabsorption and deficiencies, which affect the quality of life and increase morbidity and mortality. These guidelines were developed following the United European Gastroenterology framework for the development of high‐quality clinical guidelines. After a systematic literature review, the evidence was evaluated according to the Oxford Center for Evidence‐Based Medicine and the Grading of Recommendations Assessment, Development, and Evaluation methodology, as appropriate. Statements and comments were developed by the working groups and voted on using the Delphi method. The diagnosis of PEI should be based on a global assessment of symptoms, nutritional status, and a pancreatic secretion test. Pancreatic enzyme replacement therapy (PERT), together with dietary advice and support, are the cornerstones of PEI therapy. PERT is indicated in patients with PEI that is secondary to pancreatic disease, pancreatic surgery, or other metabolic or gastroenterological conditions. Specific recommendations concerning the management of PEI under various clinical conditions are provided based on evidence and expert opinions. This evidence‐based guideline summarizes the prevalence, clinical impact, and general diagnostic and therapeutic approaches for PEI, as well as the specifics of PEI in different clinical conditions. Finally, the unmet needs for future research are discussed.

As health care continues to evolve with technological advancements, the integration of artificial intelligence into clinical practices has shown promising potential to enhance patient care and operational efficiency. Among the forefront of these innovations are large language models (LLMs), a subset of artificial intelligence designed to understand, generate, and interact with human language at an unprecedented scale. This systematic review describes the role of LLMs in improving diagnostic accuracy, automating documentation, and advancing specialist education and patient engagement within the field of gastroenterology and gastrointestinal endoscopy. Core databases including MEDLINE through PubMed, Embase, and Cochrane Central registry were searched using keywords related to LLMs (from inception to April 2024). Studies were included if they satisfied the following criteria: (1) any type of studies that investigated the potential role of LLMs in the field of gastrointestinal endoscopy or gastroenterology, (2) studies published in English, and (3) studies in full-text format. The exclusion criteria were as follows: (1) studies that did not report the potential role of LLMs in the field of gastrointestinal endoscopy or gastroenterology, (2) case reports and review papers, (3) ineligible research objects (eg, animals or basic research), and (4) insufficient data regarding the potential role of LLMs. Risk of Bias in Non-Randomized Studies-of Interventions was used to evaluate the quality of the identified studies. Overall, 21 studies on the potential role of LLMs in gastrointestinal disorders were included in the systematic review, and narrative synthesis was done because of heterogeneity in the specified aims and methodology in each included study. The overall risk of bias was low in 5 studies and moderate in 16 studies. The ability of LLMs to spread general medical information, offer advice for consultations, generate procedure reports automatically, or draw conclusions about the presumptive diagnosis of complex medical illnesses was demonstrated by the systematic review. Despite promising benefits, such as increased efficiency and improved patient outcomes, challenges related to data privacy, accuracy, and interdisciplinary collaboration remain. We highlight the importance of navigating these challenges to fully leverage LLMs in transforming gastrointestinal endoscopy practices. PROSPERO 581772; https://www.crd.york.ac.uk/prospero/.