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BACKGROUND/AIM: The use of infliximab in severe ulcerative colitis (UC) is established; however, its role in severe acute UC requires clarification. The present multicentre case series evaluated infliximab in hospitalized patients with steroid-refractory severe UC.

Artificial intelligence (AI) is a rapidly evolving presence in all fields and industries, with the ability to both improve quality and reduce the burden of human effort. Gastroenterology is a field with a focus on diagnostic techniques and procedures, and AI and big data have established and growing roles to play. Alongside these opportunities are challenges, which will evolve in parallel., Competing Interests: Disclosure N.M. Holt: Nil; M.F. Byrne: Satisfai Health (Chairman, founder and shareholder)., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background and Aims: Multiple computer-aided detection (CADe) software programs have now achieved regulatory approval in the United States, Europe, and Asia and are being used in routine clinical practice to support colorectal cancer screening. There is uncertainty regarding how different CADe algorithms may perform. No objective methodology exists for comparing different algorithms. We aimed to identify priority scoring metrics for CADe evaluation and comparison., Methods: A modified Delphi approach was used. Twenty-five global leaders in CADe in colonoscopy, including endoscopists, researchers, and industry representatives, participated in an online survey over the course of 8 months. Participants generated 121 scoring criteria, 54 of which were deemed within the study scope and distributed for review and asynchronous e-mail-based open comment. Participants then scored criteria in order of priority on a 5-point Likert scale during ranking round 1. The top 11 highest priority criteria were re-distributed, with another opportunity for open comment, followed by a final round of priority scoring to identify the final 6 criteria., Results: Mean priority scores for the 54 criteria ranged from 2.25 to 4.38 after the first ranking round. The top 11 criteria after round 1 of ranking yielded mean priority scores ranging from 3.04 to 4.16. The final 6 highest priority criteria, including a tie for first-place ranking, were (1, tied) sensitivity (average, 4.16) and (1, tied) separate and independent validation of the CADe algorithm (average, 4.16); (3) adenoma detection rate (average, 4.08); (4) false-positive rate (average, 4.00); (5) latency (average, 3.84); and (6) adenoma miss rate (average, 3.68)., Conclusions: This is the first reported international consensus statement of priority scoring metrics for CADe in colonoscopy. These scoring criteria should inform CADe software development and refinement. Future research should validate these metrics on a benchmark video data set to develop a validated scoring instrument., Competing Interests: Disclosure The following authors disclosed financial relationships: Y. Mori: speaker fees from and consultant for Olympus Corporation; and ownership interest in Cybernet System Corp. M. Misawa: speaker fees from Olympus Corporation; ownership interest in Cybernet System Corp. J.E. East: consultant for and ownership interest in Satisfai Health; and speaker fees from Medtronic. C. Hassan: consultant for Odin, Fujifilm, Olympus, and NEC. A. Repici: consultant for Medtronic; consultant for and research support from Fujifilm; research support and speaker fees from Boston Scientific and Norgine; and research support from Olympus and Pentax. M.F. Byrne: chairman and founder: Satisfai Health. D. von Renteln: research funding from and consultant for ERBE, Pendopharm, and Fujifilm; research funding from Ventage and Pentax; and consultant for Boston Scientific. D.G. Hewett: consultant for and speaker fees from Olympus Australia Pty Ltd and Fresenius Kabi Pty Ltd; and speaker fees from Boston Scientific Pty Ltd. C. Ogawa Matsubayashi: employee and Vice President of Innovation for AI Medical Service Inc. O.F. Ahmad: speaker fees from Olympus Corporation and Medtronic. P. Sharma: consultant for Olympus Corporation, Boston Scientific, and Salix Pharmaceuticals, Cipla, Medtronic, Takeda, Samsung Bioepis, CDx, and Exact Science; and research support from Fujifilm, Erbe Medical, and Ironwood Pharmaceuticals. S.A. Gross: consultant for Cook, Medtronic, and Olympus. N. Mansour: consultant for Iterative Health. A. Cherubini: Senior Vice President of Science, AI, and Data for Cosmo Intelligent Medical Devices. N.N. Dinh: President of Cosmo Intelligent Medical Devices. X. Xiao: Co-Founder and Chief Scientist and Equity holder in Wision Ltd. P. Mountney: CEO of Odin Vision. J. González-Bueno Puyal: senior research scientist for Odin Vision. D. Zur: CEO of Magentiq Eye. H. Seibt: Global Product Manager Digital Solutions of Pentax Medical Europe. G. Little: Executive Director and Global Head of Marketing, Digital Health, for Olympus Corporation of the Americas. S. LaRocco: Vice President and Global Head, Customer Solutions Business Unit, for Olympus Corporation of the Americas. S. Conjeti: Global Product Lead for Olympus Europa SE & Co. H. Shimada: Clinical Marketing Manager for Fujifilm Healthcare Americas Corporation. T.M. Berzin: consultant for Wision AI, Medtronic, Boston Scientific, Magentiq Eye, and RSIP Vision. J.R.G. Brown: consultant for Medtronic and Olympus. All other authors disclosed no financial relationships. J.E. East is funded by the National Institute for Health ResearchOxford Biomedical Research Centre. This project was funded via the Duke Internal Medicine Residency Faculty Resident Research Grant. This project received no industry funding sources. The views expressed are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the Department of Health., (Copyright © 2024 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

Background: This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett's esophagus (BE)., Methods: 96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett's esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level., Results: AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%-74.2%] to 78.0% [95%CI 74.0%-82.0%]; specificity 67.3% [95%CI 62.5%-72.2%] to 72.7% [95%CI 68.2%-77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI., Conclusion: BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists' decisions to follow or discard AI advice., Competing Interests: N. Parsa is VP of medical affairs at Satisfai Health. M. Byrne is CEO and Founder of Satisfai Health. H. Messmann has received lecture fees from Olympus, Ambu, IPSEN, Medtronic, and Falk, and research grants from Olympus and Satisfai; he is also a consultant for Satisafi. A. Ebigbo has held lectures for Olympus, Fuji, Pentax, Medtronic, Falk, and Ambu. The remaining authors declare that they have no conflict of interest., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Background: Real-time prediction of histologic features of small colorectal polyps may prevent resection and/or pathologic evaluation and therefore decrease colonoscopy costs. Previous studies showed that computer-aided diagnosis (CADx) was highly accurate, though it did not outperform expert endoscopists., Objective: To assess the diagnostic performance of histologic predictions by general endoscopists before and after assistance from CADx in a real-life setting., Design: Prospective, multicenter, single-group study. (ClinicalTrials.gov: NCT04437615)., Setting: 6 centers across the United States., Participants: 1252 consecutive patients undergoing colonoscopy and 49 general endoscopists with variable experience in real-time prediction of polyp histologic features., Intervention: Real-time use of CADx during routine colonoscopy., Measurements: The primary end points were the sensitivity and specificity of CADx-unassisted and CADx-assisted histologic predictions for adenomas measuring 5 mm or less. For clinical purposes, additional estimates according to location and confidence level were provided., Results: The CADx device made a diagnosis for 2695 polyps measuring 5 mm or less (96%) in 1252 patients. There was no difference in sensitivity between the unassisted and assisted groups (90.7% vs. 90.8%; P = 0.52). Specificity was higher in the CADx-assisted group (59.5% vs. 64.7%; P < 0.001). Among all 2695 polyps measuring 5 mm or less, 88.2% and 86.1% ( P < 0.001) in the CADx-assisted and unassisted groups, respectively, could be resected and discarded without pathologic evaluation. Among 743 rectosigmoid polyps measuring 5 mm or less, 49.5% and 47.9% ( P < 0.001) in the CADx-assisted and unassisted groups, respectively, could be left in situ without resection., Limitation: Decision making based on CADx might differ outside a clinical trial., Conclusion: CADx assistance did not result in increased sensitivity of optical diagnosis. Despite a slight increase, the specificity of CADx-assisted diagnosis remained suboptimal., Primary Funding Source: Olympus America Corporation served as the clinical study sponsor., Competing Interests: Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M24-0086.

Since the emergence of artificial intelligence (AI) in medicine, endoscopy applications in gastroenterology have been at the forefront of innovations. The ever-increasing number of studies necessitates the need to organize and classify applications in a useful way. Separating AI capabilities by computer aided detection (CADe), diagnosis (CADx), and quality assessment (CADq) allows for a systematic evaluation of each application. CADe studies have shown promise in accurate detection of esophageal, gastric and colonic neoplasia as well as identifying sources of bleeding and Crohn's disease in the small bowel. While more advanced CADx applications employ optical biopsies to give further information to characterize neoplasia and grade inflammatory disease, diverse CADq applications ensure quality and increase the efficiency of procedures. Future applications show promise in advanced therapeutic modalities and integrated systems that provide multimodal capabilities. AI is set to revolutionize clinical decision making and performance of endoscopy., Competing Interests: None

Artificial intelligence shows promise for clinical research in inflammatory bowel disease endoscopy. Accurate assessment of endoscopic activity is important in clinical practice and inflammatory bowel disease clinical trials. Emerging artificial intelligence technologies can increase efficiency and accuracy of assessing the baseline endoscopic appearance in patients with inflammatory bowel disease and the impact that therapeutic interventions may have on mucosal healing in both of these contexts. In this review, state-of-the-art endoscopic assessment of mucosal disease activity in inflammatory bowel disease clinical trials is described, covering the potential for artificial intelligence to transform the current paradigm, its limitations, and suggested next steps. Site-based artificial intelligence quality evaluation and inclusion of patients in clinical trials without the need for a central reader is proposed; for following patient progress, a second reading using AI alongside a central reader with expedited reading is proposed. Artificial intelligence will support precision endoscopy in inflammatory bowel disease and is on the threshold of advancing inflammatory bowel disease clinical trial recruitment., (© The Author(s) 2023. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation.)

Inflammatory bowel disease encompasses Crohn's disease and ulcerative colitis and is characterized by uncontrolled, relapsing, and remitting course of inflammation in the gastrointestinal tract. Artificial intelligence represents a new era within the field of gastroenterology, and the amount of research surrounding artificial intelligence in patients with inflammatory bowel disease is on the rise. As clinical trial outcomes and treatment targets evolve in inflammatory bowel disease, artificial intelligence may prove as a valuable tool for providing accurate, consistent, and reproducible evaluations of endoscopic appearance and histologic activity, thereby optimizing the diagnosis process and identifying disease severity. Furthermore, as the applications of artificial intelligence for inflammatory bowel disease continue to expand, they may present an ideal opportunity for improving disease management by predicting treatment response to biologic therapies and for refining the standard of care by setting the basis for future treatment personalization and cost reduction. The purpose of this review is to provide an overview of the unmet needs in the management of inflammatory bowel disease in clinical practice and how artificial intelligence tools can address these gaps to transform patient care.

Background and Aims: Lack of clinical validation and inter-observer variability are two limitations of endoscopic assessment and scoring of disease severity in patients with ulcerative colitis [UC]. We developed a deep learning [DL] model to improve, accelerate and automate UC detection, and predict the Mayo Endoscopic Subscore [MES] and the Ulcerative Colitis Endoscopic Index of Severity [UCEIS]., Methods: A total of 134 prospective videos [1550 030 frames] were collected and those with poor quality were excluded. The frames were labelled by experts based on MES and UCEIS scores. The scored frames were used to create a preprocessing pipeline and train multiple convolutional neural networks [CNNs] with proprietary algorithms in order to filter, detect and assess all frames. These frames served as the input for the DL model, with the output being continuous scores for MES and UCEIS [and its components]. A graphical user interface was developed to support both labelling video sections and displaying the predicted disease severity assessment by the artificial intelligence from endoscopic recordings., Results: Mean absolute error [MAE] and mean bias were used to evaluate the distance of the continuous model's predictions from ground truth, and its possible tendency to over/under-predict were excellent for MES and UCEIS. The quadratic weighted kappa used to compare the inter-rater agreement between experts' labels and the model's predictions showed strong agreement [0.87, 0.88 at frame-level, 0.88, 0.90 at section-level and 0.90, 0.78 at video-level, for MES and UCEIS, respectively]., Conclusions: We present the first fully automated tool that improves the accuracy of the MES and UCEIS, reduces the time between video collection and review, and improves subsequent quality assurance and scoring., (© The Author(s) 2022. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Competing Interests: Intérêts concurrents: Michael Byrne est le chef de la direction, le fondateur et un actionnaire de Satisfai Health. Il est aussi le fondateur de la coentreprise AI4GI (qui a une entente de conception conjointe avec Olympus America dans le domaine de l’intelligence artificielle et des polypes colorectaux). Il a aussi déclaré une participation aux comités de la World Endoscopy Organization et de l’American Gastroenterological Association Tech Summit. Aucun autre intérêt concurrent n’a été déclaré.

Introduction: Screening-related colonoscopy is a vital component of screening initiatives to both diagnose and prevent colorectal cancer (CRC), with prevention being reliant upon early and accurate detection of pre-malignant lesions. Several strategies, techniques, and interventions exist to optimize endoscopists' adenoma detection rates (ADR)., Areas Covered: This narrative review provides an overview of the importance of ADR and other colonoscopy quality indicators. It then summarizes the available evidence regarding the effectiveness of the following domains in terms of improving ADR: endoscopist factors, pre-procedural parameters, peri-procedural parameters, intra-procedural strategies and techniques, antispasmodics, distal attachment devices, enhanced colonoscopy technologies, enhanced optics, and artificial intelligence. These summaries are based on an electronic search of the databases Embase, PubMed, and Cochrane performed on 12 December 2022., Expert Opinion: Given the prevalence and associated morbidity and mortality of CRC, the quality of screening-related colonoscopy quality is appropriately prioritized by patients, endoscopists, units, and payers alike. Endoscopists performing colonoscopy should be up to date regarding available strategies, techniques, and interventions to optimize their performance.

Competing Interests: Competing interests: Michael Byrne is the chief executive officer, founder and shareholder of Satisfai Health and founder of the AI4GI joint venture (which has a co-development agreement with Olympus America in artificial intelligence and colorectal polyps). Michael Byrne also reports participation in committees with the World Endoscopy Organization and the American Gastroenterological Association Tech Summit. No other competing interests were declared.

Competing Interests: The authors declare that they have no conflict of interest.

In 2018, the American Gastroenterological Association's Center for GI Innovation and Technology convened a consensus conference, entitled "Colorectal Cancer Screening and Surveillance: Role of Emerging Technology and Innovation to Improve Outcomes." The conference participants, which included more than 60 experts in colorectal cancer, considered recent improvements in colorectal cancer screening rates and polyp detection, persistent barriers to colonoscopy uptake, and opportunities for performance improvement and innovation. This white paper originates from that conference. It aims to summarize current patient- and physician-centered gaps and challenges in colonoscopy, diagnostic and therapeutic challenges affecting colonoscopy uptake, and the potential use of emerging technologies and quality metrics to improve patient outcomes., (Copyright © 2022 AGA Institute. All rights reserved.)

Obesity and mental health disorders are rising simultaneously with shifting dietary behavior away from home cooking, toward typically nutrition-poor and energy-dense convenience meals. Food literacy strongly influences nutrition choices. Community-based cooking interventions target barriers to healthy eating and facilitate development of food literacy skills, thereby potentially increasing preparation of home-cooked meals and positively influencing health. This study of 657 healthy Australian adults explored the efficacy of a 7-week cooking program in improving cooking confidence, whether this transferred to behavior surrounding food, and/or affected mental health. Significant post-program improvements in cooking confidence and satisfaction (all p < 0.001, η p 2 1.12 large), ability to change eating habits ( p < 0.001) and overcome lifestyle barriers ( p = 0.005) were observed for the intervention group but not control. Participation also improved mental and general health (all p < 0.05, η p 2 0.02 small). No changes were observed for acquisition and consumption of food, or nutrition knowledge in either group. This 7-week cooking program built cooking confidence and improved general and mental health but did not change dietary behavior. To further improve nutrition related behaviors associated with better mental health, more effort is needed to recruit those with below-average nutrition knowledge and interest in cooking., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Rees, Fu, Lo, Sambell, Lewis, Christophersen, Byrne, Newton, Boyle and Devine.)

Background: The accurate differentiation between T1a and T1b Barrett's-related cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an artificial intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett's cancer on white-light images., Methods: Endoscopic images from three tertiary care centers in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) were evaluated using the AI system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett's cancer., Results: The sensitivity, specificity, F1 score, and accuracy of the AI system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.74, and 0.71, respectively. There was no statistically significant difference between the performance of the AI system and that of experts, who showed sensitivity, specificity, F1, and accuracy of 0.63, 0.78, 0.67, and 0.70, respectively., Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett's cancer. AI scored equally to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and real-life settings. Nevertheless, the correct prediction of submucosal invasion in Barrett's cancer remains challenging for both experts and AI., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

BACKGROUND : Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. METHODS : An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. RESULTS : The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. CONCLUSIONS : This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy., Competing Interests: Y. Mori has received consultancy and lecture fees from Olympus Corporation. M. Misawa has received consultancy and lecture fees from Olympus Corporation. T. Berzin has received consultancy fees from Wision AI, Medtronic, and Boston Scientific. M.F. Byrne is CEO and shareholder of Satisfai Health; founder of AI4GI joint venture, a co-development agreement between Olympus America and AI4GI in artificial intelligence and colorectal polyps. J.E. East is on the clinical advisory boards of Boston Scientific, Lumendi (2017 – 2019), and Satisfai Health (2020), and has received speaker’s fees from Falk. W.E. Karnes is co-founder and chief medical officer of DocBot. A. Repici has received consultancy fees and is on the advisory board for Medtronic; he has received consultancy fees, research support, and is on the advisory board for FujiFilm. M.B. Wallace has received consultancy fees from Virgo Inc., Cosmo/Aries Pharmaceuticals, Anx Robotica (2019), Covidien, and GI Supply; he has received research grants from Fujifilm, Boston Scientific, Olympus, Medtronic, Ninepoint Medical, and Cosmo/Aries Pharmaceuticals; he also has stock/stock options in Virgo Inc.; he has provided consulting on behalf of Mayo Clinic to GI Supply (2018), Endokey, Endostart, Boston Scientific, and Microtek; he has also received general payments/minor food and beverage from Synergy Pharmaceuticals, Boston Scientific, and Cook Medical. D. Stoyanov is a shareholder in Odin Vision and Digital Surgery. L.B. Lovat is a minor shareholder and is on the advisory board of Odin Vision; he has received research grants from Medtronic, Pentax Medical, and DynamX; he is also on the advisory board for DynamX and Ninepoint Medical. O.F. Ahmad, S. Kudo, J.T. Anderson, J. Bernal, R. Bisschops, P. Chen, T. Eelbode, D.S. Elson, S.R. Gurudu, A. Histace, R. Singh, P.Valdastri and P.Wang declare that they have no conflict of interest., (© 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).)

Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the "resect-and-discard" and "diagnose-and-leave" strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence-assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists' optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of "resect-and-discard" and "diagnose-and-leave" strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field., Competing Interests: Conflict of interest statement: The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: M.F.B.: CEO and shareholder: Satisfai Health; founder of AI4GI joint venture. Co-development agreement between Olympus America and AI4GI in artificial intelligence and colorectal polyps. N.P. has no conflicts to declare., (© The Author(s) 2021.)

The American Society for Gastrointestinal Endoscopy (ASGE) has proposed the "resect-and-discard" and "diagnose-and-leave" strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the diagnostic thresholds set by these guidelines are not always met in community practice. To overcome this sub-optimal performance, artificial intelligence (AI) has been applied to the field of endoscopy. The incorporation of deep learning algorithms with AI models resulted in highly accurate systems that match the expert endoscopists' optical biopsy and exceed the ASGE recommended thresholds. Recent studies have demonstrated that the integration of AI in clinical practice results in significant improvement in endoscopists' diagnostic accuracy while reducing the time to make a diagnosis. Yet, several points need to be addressed before AI models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of AI for characterization of colorectal polyps, and review the current limitation and future directions for this field., (Copyright © 2021. Published by Elsevier Ltd.)

Capsule endoscopy is ideally suited to artificial intelligence-based interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algorithms, a form of artificial intelligence. Current software necessitates close human supervision given poor sensitivity relative to an expert reader. However, with the advent of deep learning, artificial intelligence is becoming increasingly reliable and will be increasingly relied upon. We review the major advances in artificial intelligence for capsule endoscopy in recent publications and briefly review artificial intelligence development for historical understanding. Importantly, recent advancements in artificial intelligence have not yet been incorporated into practice and it is immature to judge the potential of this technology based on current platforms. Remaining regulatory and standardization hurdles are being overcome and artificial intelligence-based clinical applications are likely to proliferate rapidly., (© 2020 Japan Gastroenterological Endoscopy Society.)

Artificial intelligence (AI) has emerged as a powerful and exciting new technology poised to impact many aspects of health care. In endoscopy, AI is now being used to detect and characterize benign and malignant GI lesions and assess malignant lesion depth of invasion. It will undoubtedly also find use in capsule endoscopy and inflammatory bowel disease. Herein, we provide the general endoscopist with a brief overview of AI and its emerging uses in our field. We also touch on the challenges of incorporating AI into clinical practice, such as workflow integration, data storage, and data privacy., (Copyright © 2020 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary costs. The quality of screening and surveillance as measured by adenoma detection rates can be improved through real-time computer-assisted detection of polyps. Unnecessary costs can be decreased with optical biopsies to identify low-risk polyps using computer-assisted diagnosis that can undergo the resect-and-discard or diagnose-and-leave strategy. Key challenges include the clinical integration of artificial intelligence-based technology into the endoscopists' workflow, the effect of this technology on endoscopy center efficiency, and the interpretability of the underlying deep learning algorithms. The future for image-based artificial intelligence in gastroenterology will include applications to improve the diagnosis and treatment of cancers throughout the gastrointestinal tract., Competing Interests: Disclosure D.L. Shung: Grant Support through the National Institutes of Health T32 DK007017; M.F. Byrne: CEO and shareholder, Satisfai Health; founder of AI4GI joint venture. Co-development agreement between Olympus America and AI4GI in artificial intelligence and colorectal polyps., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Australia, like other nations, has experienced a shift in dietary patterns away from home cooking of nutritious foods, towards a reliance on pre-prepared convenience meals. These are typically energy-dense, nutrient-poor and contribute to the rising prevalence of obesity and chronic disease burden. The aims of this study were to evaluate whether a community-based cooking program instigated a change to participants' skills, attitudes, knowledge, enjoyment and satisfaction of cooking and cooking confidence (self-efficacy)., Methods: The pseudo-random, pre-post study design consisted of an intervention and a control group. Participant recruitment and group allocation was based on their program start dates. Intervention participants were surveyed three times (baseline, 7 weeks and 6 months) and the control group were surveyed at baseline and 5 weeks. All participants were registered via an online website and were 18 years or over. Upon consent, participants were offered four levels of commitment, defined by different assessments. The minimum participation level included an online survey and levels 2, 3 and 4 involved attendance at a clinic with increasing functional, anthropometric and biomarker measurements. Primary endpoints were participants' cooking confidence as a proxy for self-efficacy. Secondary endpoints were dietary intake, physical activity levels, body composition, anthropometry, blood, urine and faecal biomarkers of systemic, physical and mental health., Discussion: The community cooking program provided participants with information and advice on food sourcing, preparation and nutrition to improve home cooking skills. The study was designed to explore whether food literacy programs are efficacious in improving participant physical health and well-being in order to combat the rise in obesity and diet-related disease. It will support future use of public health cooking program initiatives aimed at improving food literacy, self-efficacy and physical and mental health. The extensive data collected will inform future research into the relationship between diet, the gut-microbiota and human health., Trial Registration: Retrospectively registered on 16.08.2019 with the Australian New Zealand Clinical Trials Registry (ANZCTR). ACTRN12619001144101 . Protocol version 4.

Competing Interests: Michael Byrne is the founder of “ai4gi,” a commercial joint venture that has a co-development agreement with Olympus America. He is CEO and a shareholder of Satis Operations Inc., part of the “ai4gi” joint venture.

Background: In general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the 'resect and discard' paradigm for diminutive colorectal polyps workable. Computer analysis of video could potentially eliminate the obstacle of interobserver variability in endoscopic polyp interpretation and enable widespread acceptance of 'resect and discard'., Study Design and Methods: We developed an artificial intelligence (AI) model for real-time assessment of endoscopic video images of colorectal polyps. A deep convolutional neural network model was used. Only narrow band imaging video frames were used, split equally between relevant multiclasses. Unaltered videos from routine exams not specifically designed or adapted for AI classification were used to train and validate the model. The model was tested on a separate series of 125 videos of consecutively encountered diminutive polyps that were proven to be adenomas or hyperplastic polyps., Results: The AI model works with a confidence mechanism and did not generate sufficient confidence to predict the histology of 19 polyps in the test set, representing 15% of the polyps. For the remaining 106 diminutive polyps, the accuracy of the model was 94% (95% CI 86% to 97%), the sensitivity for identification of adenomas was 98% (95% CI 92% to 100%), specificity was 83% (95% CI 67% to 93%), negative predictive value 97% and positive predictive value 90%., Conclusions: An AI model trained on endoscopic video can differentiate diminutive adenomas from hyperplastic polyps with high accuracy. Additional study of this programme in a live patient clinical trial setting to address resect and discard is planned., Competing Interests: Competing interests: MFB: CEO and shareholder, Satis Operations Inc, ’ai4gi’ joint venture; research support: Boston Scientific. NC: Imagia shareholder, ‘ai4gi’ joint venture. FS: Imagia shareholder, ‘ai4gi’ joint venture. CO: Imagia shareholder, ‘ai4gi' joint venture. FC: Imagia shareholder, ’ai4gi' joint venture. DKR: consultant: Olympus Corp and Boston Scientific; research support: Boston Scientific, Endochoice and EndoAid., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2019. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Background: Endoscopic ultrasound-guided fine-needle aspiration biopsy (EUS-FNA) is the preferred method for biopsying the gastrointestinal tract, and rapid on-site cytological evaluation is considered standard practice. Our institution does not perform on-site evaluation; this study analyzes our overall diagnostic yield, accuracy, and incidence of nondiagnostic cases to determine the validity of this strategy., Design: Data encompassing clinical information, procedural records, and cytological assessment were analyzed for gastrointestinal EUS-FNA procedures (n = 85) performed at Vancouver General Hospital from January 2012 to January 2013. We compared our results with those of studies that had on-site evaluation and studies that did not have on-site evaluation., Results: Eighty-five biopsies were performed in 78 patients, from sites that included the pancreas, the stomach, the duodenum, lymph nodes, and retroperitoneal masses. Malignancies were diagnosed in 45 (53%) biopsies, while 24 (29%) encompassed benign entities. Suspicious and atypical results were recorded in 8 (9%) and 6 (7%) cases, respectively. Only 2 (2%) cases received a cytological diagnosis of 'nondiagnostic'. Our overall accuracy was 72%, our diagnostic yield was 98%, and our nondiagnostic rate was 2%. Our results did not significantly differ from those of studies that did have on-site evaluation., Conclusion: Our study highlights that adequate diagnostic accuracy can be achieved without on-site evaluation., (© 2015 S. Karger AG, Basel.)

When endoscopic retrograde cholangio-pancreatography fails to decompress the pancreatic or biliary system, alternative interventions are required. In this situation, endosonography guided cholangio-pancreatography (ESCP), percutaneous radiological therapy or surgery can be considered. Small case series reporting the initial experience with ESCP have been superseded by comprehensive reports of large cohorts. Although these reports are predominantly retrospective, they demonstrate that endoscopic ultrasound (EUS) guided biliary and pancreatic interventions are associated with high levels of technical and clinical success. The procedural complication rates are lower than those seen with percutaneous therapy or surgery. This article describes and discusses data published in the last five years relating to EUS-guided biliary and pancreatic intervention.

Helicobacter pylori (H. pylori) infection has been clearly linked to peptic ulcer disease and some gastrointestinal malignancies. Increasing evidence demonstrates possible associations to disease states in other organ systems, known as the extraintestinal manifestations of H. pylori. Different conditions associated with H. pylori infection include those from hematologic, cardiopulmonary, metabolic, neurologic, and dermatologic systems. The aim of this article is to provide a concise review of the evidence that supports or refutes the associations of H. pylori and its proposed extraintestinal manifestations. Based on data from the literature, PUD, mucosal associated lymphoid tumors lymphoma, and gastric adenocarcinoma has well-established links. Current evidence most supports extraintestinal manifestations with H. pylori in immune thrombocytopenic purpura, iron deficiency anemia, urticaria, Parkinson's, migraines and rosacea; however, there is still plausible link with other diseases that requires further research.

Background: Endoscopic therapy has been successful in the management of biliary complications after both deceased donor liver transplantation (DDLT) and living donor liver transplantation (LDLT). LDLT is thought to be associated with higher rates of biliary complications, but there are few studies comparing the success of endoscopic management of anastomotic strictures between the two groups. This study aims to compare our experience in the endoscopic management of anastomotic strictures in DDLT versus LDLT., Methods: This is a retrospective database review of all liver transplant patients undergoing endoscopic retrograde cholangiopancreatography (ERCP) after liver transplantation. The frequency of anastomotic stricture and the time to develop and to resolve anastomotic stricture were compared between DDLT and LDLT. The response of anastomotic stricture to endoscopic therapy was also analyzed., Results: A total of 362 patients underwent liver transplantation between 2003 and 2011, with 125 requiring ERCP to manage biliary complications. Thirty-three (9.9%) cases of DDLT and 8 (27.6%) of LDLT (P=0.01) were found to have anastomotic stricture. When comparing DDLT and LDLT, there was no difference in the mean time to the development of anastomotic strictures (98+/-17 vs 172+/-65 days, P=0.11), likelihood of response to ERCP [22 (66.7%) vs 6 (75.0%), P=0.69], mean time to the resolution of anastomotic strictures (268+/-77 vs 125+/-37 days, P=0.34), and the number of ERCPs required to achieve resolution (3.9+/-0.4 vs 4.7+/-0.9, P=0.38)., Conclusions: Endoscopic therapy is effective in the majority of biliary complications relating to liver transplantation. Anastomotic strictures occur more frequently in LDLT compared with DDLT, with equivalent endoscopic treatment response and outcomes for both groups.

Introduction: A needle knife is often used to gain bile duct access when standard techniques have failed. If unsuccessful, the next step may involve either radiological- or endoscopic ultrasound-guided biliary access. However, repeat endoscopic retrograde cholangiopancreatography (ERCP) may be an option if the patient's clinical condition permits., Aim: To determine the success of repeat ERCP after failed use of a needle knife to gain biliary access., Methods: Retrospective analysis of all patients who underwent initial unsuccessful biliary cannulation after use of a needle knife between 2007 and 2010., Results: Seventy five patients were identified. Of these, 51 (68%) underwent repeat ERCP, and biliary cannulation was successful in 38 (75%). The median time to repeat ERCP was 7.7 days (range 1-28 days). Complications developed in two (4%) patients. These included one case each of wire-guided perforation and mild pancreatitis, both of which were resolved by conservative management., Conclusions: Repeat ERCP within a few days after failed use of a needle knife for biliary access is associated with acceptable success and acceptable incidence of complications, and therefore obviates the need for alternative approaches for biliary access for most patients.

Background: Stent related occlusion and migration remains a problem despite attempts to improve stent design over this time period. Flanged polyethylene plastic stents (FPS) remains the stent of choice in most centers. Early failure of stents placed for malignant extrahepatic biliary strictures (MEBS) has not previously been studied in detail. We set out to determine the incidence and reasons for biliary stent change within 30 days of the index procedure in a large tertiary center population during a period where (FPS) was the sole plastic stent used., Methods: Retrospective analysis of endoscopic retrograde cholangiography (ERCP) was undertaken in patients who were stented for presumed or known MEBS between 1993 and 2001. Patients who required repeat stenting within 30 days were identified., Results: All 508 patients were stented for MEBS. 5.7% of patients had a total of 34 repeat stenting procedures within 30 days of the index procedure; 27of 29 index stents were plastic, 2 were self-expandable metal stents (SEMS), 20 (3.9%) patients had stent failure as the reason for a stent exchange (plastic stent occlusion n = 15, mean time to stent change 14 ± 8.3 days; metal stent occlusion n = 2, mean time to stent change 24.5 ± 7.8 days; plastic stent migration n = 3, mean time to stent change 25 ± 5.3 days). There was a statistically significant difference in the time to stent change between the occluded plastic stent and migrated plastic stent cases (P = 0.045, 95% CI -21.7 to -0.29). 6 patients spent at least 2 additional days in hospital as a result of stent failure., Conclusions: Early stent failure is an uncommon problem, especially in patients with SEMS. Early plastic stent failure appears to occur sooner with stent occlusion than with stent migration. Early stent failure is associated with significant morbidity and bears an economic impact in additional procedures and hospital stay.

Biliary extraction baskets are a commonly used instrument for the removal of choledocholithiasis in endoscopic retrograde cholangiopancreatography (ERCP). Impaction of the extraction basket is a recognized complication of ERCP, and is usually the result of discrepancy between the size of bile duct stone and the diameter of the distal bile duct. Whilst mechanical lithotriptors can be used to crush the stone or break the wires of the basket to allow its release, failure of the lithotriptor device can occur. We describe the case of a 59-year-old gentleman who had an ERCP performed for choledocholithiasis. Basket impaction was encountered, and the mechanical lithotriptor failed to dislodge the stone/basket complex. A two-step technique involving balloon dilatation and forceps manipulation of the basket was applied to successfully dislodge the impacted basket. We believe this simple and safe technique should be adopted to rescue impacted biliary extraction baskets to avoid the need for potential surgical removal.

Pancreatitis is the most common complication of ERCP. It can be associated with substantial morbidity. Hence, the minimization of both the incidence and severity of post-ERCP pancreatitis is paramount. Considerable efforts have been made to identify factors that may be associated with an increased risk of this complication. In addition, both procedure- and pharmacological-related interventions have been proposed that may prevent this complication. This paper outlines these interventions and presents the evidence to support their use in the prevention of post-ERCP pancreatitis.

Endothelin receptor antagonists are commonly used in the treatment of pulmonary hypertension. Sitaxsentan, a selective endothelin A receptor blocker, induces a mild transaminitis in approximately 3% to 5% of patients, but rarely an acute severe hepatitis. A case involving a 61-year-old female with sitaxsentan-induced acute severe liver failure is presented. Depite withdrawal of therapy, her liver tests failed to improve. After six weeks of monitoring, the patient was administered high-dose corticosteroids, with a good clinical and biochemical response. While endothelin receptor antagonists are postulated to cause hepatitis by inhibition of a bile salt transporter pump, an immune-mediated or idiosyncratic mechanism should be considered.