Computer-Aided Diagnosis for Leaving Colorectal Polyps In Situ : A Systematic Review and Meta-analysis.

BACKGROUND: Computer-aided diagnosis (CADx) allows prediction of polyp histology during colonoscopy, which may reduce unnecessary removal of nonneoplastic polyps. However, the potential benefits and harms of CADx are still unclear. PURPOSE: To quantify the benefit and harm of using CADx in colonoscopy for the optical diagnosis of small (≤5-mm) rectosigmoid polyps. DATA SOURCES: Medline, Embase, and Scopus were searched for articles published before 22 December 2023. STUDY SELECTION: Histologically verified diagnostic accuracy studies that evaluated the real-time performance of physicians in predicting neoplastic change of small rectosigmoid polyps without or with CADx assistance during colonoscopy. DATA EXTRACTION: The clinical benefit and harm were estimated on the basis of accuracy values of the endoscopist before and after CADx assistance. The certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework. The outcome measure for benefit was the proportion of polyps predicted to be nonneoplastic that would avoid removal with the use of CADx. The outcome measure for harm was the proportion of neoplastic polyps that would be not resected and left in situ due to an incorrect diagnosis with the use of CADx. Histology served as the reference standard for both outcomes. DATA SYNTHESIS: Ten studies, including 3620 patients with 4103 small rectosigmoid polyps, were analyzed. The studies that assessed the performance of CADx alone (9 studies; 3237 polyps) showed a sensitivity of 87.3% (95% CI, 79.2% to 92.5%) and specificity of 88.9% (CI, 81.7% to 93.5%) in predicting neoplastic change. In the studies that compared histology prediction performance before versus after CADx assistance (4 studies; 2503 polyps), there was no difference in the proportion of polyps predicted to be nonneoplastic that would avoid removal (55.4% vs. 58.4%; risk ratio [RR], 1.06 [CI, 0.96 to 1.17]; moderate-certainty evidence) or in the proportion of neoplastic polyps that would be erroneously left in situ (8.2% vs. 7.5%; RR, 0.95 [CI, 0.69 to 1.33]; moderate-certainty evidence). LIMITATION: The application of optical diagnosis was only simulated, potentially altering the decision-making process of the operator. CONCLUSION: Computer-aided diagnosis provided no incremental benefit or harm in the management of small rectosigmoid polyps during colonoscopy. PRIMARY FUNDING SOURCE: European Commission. (PROSPERO: CRD42023402197).

QUAIDE - Quality assessment of AI preclinical studies in diagnostic endoscopy.

Artificial intelligence (AI) holds significant potential for enhancing quality of gastrointestinal (GI) endoscopy, but the adoption of AI in clinical practice is hampered by the lack of rigorous standardisation and development methodology ensuring generalisability. The aim of the Quality Assessment of pre-clinical AI studies in Diagnostic Endoscopy (QUAIDE) Explanation and Checklist was to develop recommendations for standardised design and reporting of preclinical AI studies in GI endoscopy.The recommendations were developed based on a formal consensus approach with an international multidisciplinary panel of 32 experts among endoscopists and computer scientists. The Delphi methodology was employed to achieve consensus on statements, with a predetermined threshold of 80% agreement. A maximum three rounds of voting were permitted.Consensus was reached on 18 key recommendations, covering 6 key domains: data acquisition and annotation (6 statements), outcome reporting (3 statements), experimental setup and algorithm architecture (4 statements) and result presentation and interpretation (5 statements). QUAIDE provides recommendations on how to properly design (1. Methods, statements 1-14), present results (2. Results, statements 15-16) and integrate and interpret the obtained results (3. Discussion, statements 17-18).The QUAIDE framework offers practical guidance for authors, readers, editors and reviewers involved in AI preclinical studies in GI endoscopy, aiming at improving design and reporting, thereby promoting research standardisation and accelerating the translation of AI innovations into clinical practice.

AGA Clinical Practice Update on the Role of Artificial Intelligence in Colon Polyp Diagnosis and Management: Commentary.

DESCRIPTION: The purpose of this American Gastroenterological Association (AGA) Institute Clinical Practice Update (CPU) is to review the available evidence and provide expert commentary on the current landscape of artificial intelligence in the evaluation and management of colorectal polyps. METHODS: This CPU was commissioned and approved by the AGA Institute Clinical Practice Updates Committee (CPUC) and the AGA Governing Board to provide timely guidance on a topic of high clinical importance to the AGA membership and underwent internal peer review by the CPUC and external peer review through standard procedures of Gastroenterology. This Expert Commentary incorporates important as well as recently published studies in this field, and it reflects the experiences of the authors who are experienced endoscopists with expertise in the field of artificial intelligence and colorectal polyps.

Risk of Pancreatitis After Endoscopic Ultrasound-Guided Fine-Needle Aspiration of Pancreatic Cystic Lesions: A Systematic Review and Meta-Analysis.

INTRODUCTION: Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is frequently used to risk-stratify pancreatic cystic lesions (PCLs). Rising PCL incidence and developments in tissue acquisition and specimen analysis necessitate updated appraisal of EUS-FNA safety, particularly the risk of postprocedure pancreatitis, the most common EUS-FNA-related adverse event. Our systematic review aims to accurately quantify the risk of EUS-FNA-related pancreatitis to best inform decisions regarding EUS-FNA's optimal role in PCL workup. METHODS: We performed systematic searches in 4 databases from inception to April 2024 for original English-language studies investigating EUS-FNA-related pancreatitis. We extracted data on demographics and EUS-FNA-related pancreatitis risk, severity, and risk factors. These were meta-analyzed through the DerSimonian Laird Method using a random-effects model. Meta-regression of pancreatitis risk was performed to delineate associations with clinical and procedural characteristics. RESULTS: Sixty-four studies comprised 8,086 patients and reported 110 EUS-FNA-related pancreatitis events. Pooled risk of EUS-FNA-related pancreatitis was 1.4% (95% confidence intervals, -0.8% to 3.5%; I2 = 0.00), which was predominantly of mild severity (67%) and uniformly nonfatal. Pancreatitis risk lacked significant association with sample size, age, sex, cyst size, needle caliber, or passes, although we noted trends toward higher risk in studies published after 2015, those using higher gauge needles (19 G vs 22 G/25 G), and those performing EUS-guided through-the-needle biopsy. DISCUSSION: We note with high certainty that pancreatitis after EUS-FNA of PCLs is infrequent and mild in severity with no mortality in the included cohort. EUS-guided through-the-needle biopsy may serve as a significant risk factor for EUS-FNA-related pancreatitis risk; however, further studies are needed to delineate other predisposing characteristics.

Physician perceptions on the current and future impact of artificial intelligence to the field of gastroenterology.

BACKGROUND AND AIMS: The use of artificial intelligence (AI) has transformative implications to the practice of gastroenterology and endoscopy. The aims of this study were to understand the perceptions of the gastroenterology community toward AI and to identify potential barriers for adoption. METHODS: A 16-question online survey exploring perceptions on the current and future implications of AI to the field of gastroenterology was developed by the American Society for Gastrointestinal Endoscopy AI Task Force and distributed to national and international society members. Participant demographic information including age, sex, experience level, and practice setting was collected. Descriptive statistics were used to summarize survey findings, and a Pearson χ2 analysis was performed to determine the association between participant demographic information and perceptions of AI. RESULTS: Of 10,162 invited gastroenterologists, 374 completed the survey. The mean age of participants was 46 years (standard deviation, 12), and 299 participants (80.0%) were men. One hundred seventy-nine participants (47.9%) had >10 years of practice experience, with nearly half working in the community setting. Only 25 participants (6.7%) reported the current use of AI in their clinical practice. Most participants (95.5%) believed that AI solutions will have a positive impact in their practice. One hundred seventy-six participants (47.1%) believed that AI will make clinical duties more technical but will also ease the burden of the electronic medical record (54.0%). The top 3 areas where AI was predicted to be most influential were endoscopic lesion detection (65.3%), endoscopic lesion characterization (65.8%), and quality metrics (32.6%). Participants voiced a desire for education on topics such as the clinical use of AI applications (64.4%), the advantages and limitations of AI applications (57.0%), and the technical methodology of AI (44.7%). Most participants (42.8%) expressed that the cost of AI implementation should be covered by their hospital. Demographic characteristics significantly associated with this perception included participants' years in practice and practice setting. CONCLUSIONS: Gastroenterologists have an overall positive perception regarding the use of AI in clinical practice but voiced concerns regarding its technical aspects and coverage of costs associated with implementation. Further education on the clinical use of AI applications with understanding of the advantages and limitations appears to be valuable in promoting adoption.

Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology.

BACKGROUND AND AIMS: The American Society for Gastrointestinal Endoscopy (ASGE) AI Task Force along with experts in endoscopy, technology space, regulatory authorities, and other medical subspecialties initiated a consensus process that analyzed the current literature, highlighted potential areas, and outlined the necessary research in artificial intelligence (AI) to allow a clearer understanding of AI as it pertains to endoscopy currently. METHODS: A modified Delphi process was used to develop these consensus statements. RESULTS: Statement 1: Current advances in AI allow for the development of AI-based algorithms that can be applied to endoscopy to augment endoscopist performance in detection and characterization of endoscopic lesions. Statement 2: Computer vision-based algorithms provide opportunities to redefine quality metrics in endoscopy using AI, which can be standardized and can reduce subjectivity in reporting quality metrics. Natural language processing-based algorithms can help with the data abstraction needed for reporting current quality metrics in GI endoscopy effortlessly. Statement 3: AI technologies can support smart endoscopy suites, which may help optimize workflows in the endoscopy suite, including automated documentation. Statement 4: Using AI and machine learning helps in predictive modeling, diagnosis, and prognostication. High-quality data with multidimensionality are needed for risk prediction, prognostication of specific clinical conditions, and their outcomes when using machine learning methods. Statement 5: Big data and cloud-based tools can help advance clinical research in gastroenterology. Multimodal data are key to understanding the maximal extent of the disease state and unlocking treatment options. Statement 6: Understanding how to evaluate AI algorithms in the gastroenterology literature and clinical trials is important for gastroenterologists, trainees, and researchers, and hence education efforts by GI societies are needed. Statement 7: Several challenges regarding integrating AI solutions into the clinical practice of endoscopy exist, including understanding the role of human-AI interaction. Transparency, interpretability, and explainability of AI algorithms play a key role in their clinical adoption in GI endoscopy. Developing appropriate AI governance, data procurement, and tools needed for the AI lifecycle are critical for the successful implementation of AI into clinical practice. Statement 8: For payment of AI in endoscopy, a thorough evaluation of the potential value proposition for AI systems may help guide purchasing decisions in endoscopy. Reliable cost-effectiveness studies to guide reimbursement are needed. Statement 9: Relevant clinical outcomes and performance metrics for AI in gastroenterology are currently not well defined. To improve the quality and interpretability of research in the field, steps need to be taken to define these evidence standards. Statement 10: A balanced view of AI technologies and active collaboration between the medical technology industry, computer scientists, gastroenterologists, and researchers are critical for the meaningful advancement of AI in gastroenterology. CONCLUSIONS: The consensus process led by the ASGE AI Task Force and experts from various disciplines has shed light on the potential of AI in endoscopy and gastroenterology. AI-based algorithms have shown promise in augmenting endoscopist performance, redefining quality metrics, optimizing workflows, and aiding in predictive modeling and diagnosis. However, challenges remain in evaluating AI algorithms, ensuring transparency and interpretability, addressing governance and data procurement, determining payment models, defining relevant clinical outcomes, and fostering collaboration between stakeholders. Addressing these challenges while maintaining a balanced perspective is crucial for the meaningful advancement of AI in gastroenterology.

Development of American Society for Gastrointestinal Endoscopy standards for training in advanced endoscopy within dedicated advanced endoscopy fellowship programs.

BACKGROUND AND AIMS: Training in interventional endoscopy is offered by nonaccredited advanced endoscopy fellowship programs (AEFPs). The number of these programs has increased dramatically with a concurrent increase in the breadth and complexity of interventional endoscopy procedures. Accreditation is governed by competency-based education, yet what constitutes a "high-quality" nonaccredited AEFP has not been defined. Using an evidence-based consensus process, we aimed to establish standards for AEFPs. METHODS: The RAND UCLA appropriateness method, a well-described modified Delphi process to develop quality indicators, was used. A task force established by the American Society for Gastrointestinal Endoscopy drafted potential quality indicators (structure, process, and outcome) in 6 categories: activity preceding training; structure of AEFPs; training in ERCP, EUS, and EMR; and luminal stent placement. Three rounds of iterative feedback from 20 experts were conducted. Round 0 involved discussion of project details. In round 1, experts independently ranked proposed quality indicators on a 9-point interval scale ranging from highly inappropriate (1) to highly appropriate (9). Next, proposed quality indicators were discussed and reworded in a group meeting followed by round 2, in which experts independently reranked proposed quality indicators and provided benchmarks (when applicable). The median score for each quality indicator was calculated. Mean absolute deviation from the median was calculated, and appropriateness of potential quality indicators was assessed using the BIOMED concerted action on appropriateness definition, P value method, and interpercentile range adjusted for symmetry definition. A quality indicator was deemed appropriate if the median score was ≥7 and met criteria for appropriateness using all 3 defined statistical methods. RESULTS: Of 89 proposed quality indicators, 37 statements met criteria as appropriate for a quality indicator (activity preceding training, 2; structure of AEFPs, 10; training in ERCP, 7; training in EUS, 8; training in EMR, 7; luminal stent placement, 3). Minimum thresholds were defined for 19 relevant quality indicators for number of trainers, procedures during fellowship, and procedures before assessment of competence. Among the final appropriate quality indicators were that all trainees should undergo qualitative and quantitative competence assessments using validated tools at least quarterly with documented feedback throughout the training period and that trainees should track outcomes and relevant quality metrics for specific procedures. CONCLUSIONS: This consensus process using validated methodology established standards for an AEFP in an effort to ensure adequate training in the most commonly taught interventional endoscopic procedures (ERCP, EUS, EMR, and luminal stent placement) during fellowship. An important component of an AEFP is the use of competency-based assessments that are compliant with the Accreditation Council for Graduate Medical Education's Next Accreditation System, with the goal of ensuring that trainees achieve specific milestones in their progression to achieving cognitive and technical competency.

Efficacy, Feasibility, and Safety of the X-Tack Endoscopic HeliX Tacking System: A Multicenter Experience.

BACKGROUND AND AIMS: The application of endoscopic suturing has revolutionized defect closures. Conventional over-the-scope suturing necessitates removal of the scope, placement of the device, and reinsertion. A single channel, single sequence, through-the-scope suturing device has been developed to improve this process. This study aims to describe the efficacy, feasibility, and safety of a through-the-scope suturing device for gastrointestinal defect closure. METHODS: This was a retrospective multicenter study involving 9 centers of consecutive adult patients who underwent suturing using the X-Tack Endoscopic HeliX Tacking System (Apollo Endosurgery). The primary outcomes were technical success and long-term clinical success. Secondary outcomes included adverse events, recurrence, and reintervention rates. RESULTS: In all, 56 patients (mean age 53.8, 33 women) were included. Suturing indications included fistula repair (n=22), leak repair (n=7), polypectomy defect closure (n=12), peroral endoscopic myotomy (POEM) site closure (n=7), perforation repair (n=6), and ulcers (n=2). Patients were followed at a mean duration of 74 days. Overall technical and long-term clinical success rates were 92.9% and 75%, respectively. Both technical and clinical success rates were 100% for polypectomies, POEM-site closures, and ulcers. Success rates were lower for the repair of fistulas (95.5% technical, 54.5% clinical), leaks (57.1%, 28.6%), and perforations (100%, 66.7%). No immediate adverse events were noted. CONCLUSION: This novel, through-the-scope endoscopic suturing system, is a safe and feasible method to repair defects that are ≤3 cm. The efficacy of this device may be better suited for superficial defects as opposed to full-thickness defects. Larger defects will need more sutures and probably a double closure technique to provide a reinforcement layer.

Comparative Risks of Post-ERCP Adverse Events in Patients with Asymptomatic and Symptomatic Choledocholithiasis: A Systematic Review and Meta-Analysis.

BACKGROUND AND AIMS: Endoscopic retrograde cholangiopancreatography (ERCP) is the standard of care for the management of choledocholithiasis but carries risk of complications which may result in significant morbidity and mortality. While currently available guidelines endorse the use of ERCP for the management of symptomatic common bile duct stones, the need for ERCP in incidentally found asymptomatic choledocholithiasis is more controversial, and practice varies on a geographic and institutional level. This systematic review and meta-analysis is conducted to compare post-ERCP adverse events between asymptomatic and symptomatic choledocholithiasis patients. METHODS: We searched PubMed/Embase/Web of Science databases to include all studies comparing post-ERCP outcomes between asymptomatic and symptomatic choledocholithiasis patients. The primary outcome was post-ERCP pancreatitis (PEP), while secondary outcomes included post-ERCP cholangitis, bleeding, and perforation. We calculated pooled risk ratios (RR) and 95% confidence intervals (CIs) using the Mantel-Haenszel method within a random-effect model. RESULTS: Our analysis included six observational studies, totaling 2,178 choledocholithiasis patients (392 asymptomatic and 1786 symptomatic); 53% were female. Asymptomatic patients exhibited a higher risk of PEP compared with symptomatic patients (11.7% versus 4.8%; RR 2.59, 95% CI 1.56-4.31, p ≤ 0.001). No significant difference was observed in post-ERCP cholangitis, bleeding, or perforation rates between the two groups. CONCLUSIONS: Asymptomatic patients with choledocholithiasis appear to have a higher risk of PEP than symptomatic patients, while the risk of other post-ERCP adverse events is similar between the two groups. Interventional endoscopists should thoroughly discuss potential adverse events (particularly PEP) with asymptomatic patients before performing ERCP and utilize PEP-prevention measures more liberally in this subgroup of patients.

Impact of Artificial Intelligence on Colonoscopy Surveillance After Polyp Removal: A Pooled Analysis of Randomized Trials.

BACKGROUND AND AIMS: Artificial intelligence (AI) tools aimed at improving polyp detection have been shown to increase the adenoma detection rate during colonoscopy. However, it is unknown how increased polyp detection rates by AI affect the burden of patient surveillance after polyp removal. METHODS: We conducted a pooled analysis of 9 randomized controlled trials (5 in China, 2 in Italy, 1 in Japan, and 1 in the United States) comparing colonoscopy with or without AI detection aids. The primary outcome was the proportion of patients recommended to undergo intensive surveillance (ie, 3-year interval). We analyzed intervals for AI and non-AI colonoscopies for the U.S. and European recommendations separately. We estimated proportions by calculating relative risks using the Mantel-Haenszel method. RESULTS: A total of 5796 patients (51% male, mean 53 years of age) were included; 2894 underwent AI-assisted colonoscopy and 2902 non-AI colonoscopy. When following U.S. guidelines, the proportion of patients recommended intensive surveillance increased from 8.4% (95% CI, 7.4%-9.5%) in the non-AI group to 11.3% (95% CI, 10.2%-12.6%) in the AI group (absolute difference, 2.9% [95% CI, 1.4%-4.4%]; risk ratio, 1.35 [95% CI, 1.16-1.57]). When following European guidelines, it increased from 6.1% (95% CI, 5.3%-7.0%) to 7.4% (95% CI, 6.5%-8.4%) (absolute difference, 1.3% [95% CI, 0.01%-2.6%]; risk ratio, 1.22 [95% CI, 1.01-1.47]). CONCLUSIONS: The use of AI during colonoscopy increased the proportion of patients requiring intensive colonoscopy surveillance by approximately 35% in the United States and 20% in Europe (absolute increases of 2.9% and 1.3%, respectively). While this may contribute to improved cancer prevention, it significantly adds patient burden and healthcare costs.

Framework and metrics for the clinical use and implementation of artificial intelligence algorithms into endoscopy practice: recommendations from the American Society for Gastrointestinal Endoscopy Artificial Intelligence Task Force.

In the past few years, we have seen a surge in the development of relevant artificial intelligence (AI) algorithms addressing a variety of needs in GI endoscopy. To accept AI algorithms into clinical practice, their effectiveness, clinical value, and reliability need to be rigorously assessed. In this article, we provide a guiding framework for all stakeholders in the endoscopy AI ecosystem regarding the standards, metrics, and evaluation methods for emerging and existing AI applications to aid in their clinical adoption and implementation. We also provide guidance and best practices for evaluation of AI technologies as they mature in the endoscopy space. Note, this is a living document; periodic updates will be published as progress is made and applications evolve in the field of AI in endoscopy.

Novel through-the-scope suture closure of colonic EMR defects (with video).

BACKGROUND AND AIMS: Large colon polyps removed by EMR can be complicated by delayed bleeding. Prophylactic defect clip closure can reduce post-EMR bleeding. Larger defects can be challenging to close using through-the-scope clips (TTSCs), and proximal defects are difficult to reach using over-the-scope techniques. A novel, through-the-scope suturing (TTSS) device allows direct closure of mucosal defects without scope withdrawal. The goal of this study was to evaluate the rate of delayed bleeding after the closure of large colon polyp EMR sites with TTSS. METHODS: A multicenter retrospective cohort study was performed involving 13 centers. All defect closure by TTSS after EMR of colon polyps ≥2 cm from January 2021 to February 2022 were included. The primary outcome was rate of delayed bleeding. RESULTS: A total of 94 patients (52% female; mean age, 65 years) underwent EMR of predominantly right-sided (n = 62 [66%]) colon polyps (median size, 35 mm; interquartile range, 30-40 mm) followed by defect closure with TTSS during the study period. All defects were successfully closed with TTSS alone (n = 62 [66%]) or with TTSS and TTSCs (n = 32 [34%]), using a median of 1 (interquartile range, 1-1) TTSS system. Delayed bleeding occurred in 3 patients (3.2%), with 2 requiring repeated endoscopic evaluation/treatment (moderate). CONCLUSION: TTSS alone or with TTSCs was effective in achieving complete closure of all post-EMR defects, despite a large lesion size. After TTSS closure with or without adjunctive devices, delayed bleeding was seen in 3.2% of cases. Further prospective studies are needed to validate these findings before wider adoption of TTSS for large polypectomy closure.

Speedometer for withdrawal time monitoring during colonoscopy: a clinical implementation trial.

OBJECTIVES: Meticulous inspection of the mucosa during colonoscopy, represents a lengthier withdrawal time, but has been shown to increase adenoma detection rate (ADR). We investigated if artificial intelligence-aided speed monitoring can improve suboptimal withdrawal time. METHODS: We evaluated the implementation of a computer-aided speed monitoring device during colonoscopy at a large academic endoscopy center. After informed consent, patients ≥18 years undergoing colonoscopy between 5 March and 29 April 2021 were examined without the use of the speedometer, and with the speedometer between 29 April and 30 June 2021. All colonoscopies were recorded, and withdrawal time was assessed based on the recordings in a blinded fashion. We compared mean withdrawal time, percentage of withdrawal time ≥6 min, and ADR with and without the speedometer. RESULTS: One hundred sixty-six patients in each group were eligible for analyses. Mean withdrawal time was 9 min and 6.6 s (95% CI: 8 min and 34.8 s to 9 min and 39 s) without the use of the speedometer, and 9 min and 9 s (95% CI: 8 min and 45 s to 9 min and 33.6 s) with the speedometer; difference 2.3 s (95% CI: -42.3-37.7, p = 0.91). The ADRs were 45.2% (95% CI: 37.6-52.8) without the speedometer as compared to 45.8% (95% CI: 38.2-53.4) with the speedometer (p = 0.91). The proportion of colonoscopies with withdrawal time ≥6 min without the speedometer was 85.5% (95% CI: 80.2-90.9) versus 86.7% (95% CI: 81.6-91.9) with the speedometer (p = 0.75). CONCLUSIONS: Use of speed monitoring during withdrawal did not increase withdrawal time or ADR in colonoscopy. CLINICALTRIALS.GOV IDENTIFIER: NCT04710251.

Consensus guidelines for the perioperative management of patients undergoing endoscopic retrograde cholangiopancreatography.

Deep sedation without tracheal intubation (monitored anaesthesia care) and general anaesthesia with tracheal intubation are commonly used anaesthesia techniques for endoscopic retrograde cholangiopancreatography (ERCP). There are distinct pathophysiological differences between monitored anaesthesia care and general anaesthesia that need to be considered depending on the nature and severity of the patient's underlying disease, comorbidities, and procedural risks. An international group of expert anaesthesiologists and gastroenterologists created clinically relevant questions regarding the merits and risks of monitored anaesthesia care vs general anaesthesia in specific clinical scenarios for planning optimal anaesthetic approaches for ERCP. Using a modified Delphi approach, the group created practical recommendations for anaesthesiologists, with the aim of reducing the incidence of perioperative adverse outcomes while maximising healthcare resource utilisation. In the majority of clinical scenarios analysed, our expert recommendations favour monitored anaesthesia care over general anaesthesia. Patients with increased risk of pulmonary aspiration and those undergoing prolonged procedures of high complexity were thought to benefit from general anaesthesia with tracheal intubation. Patient age and ASA physical status were not considered to be factors for choosing between monitored anaesthesia care and general anaesthesia. Monitored anaesthesia care is the favoured anaesthesia plan for ERCP. An individual risk-benefit analysis that takes into account provider and institutional experience, patient comorbidities, and procedural risks is also needed.

Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement.

The number of artificial intelligence (AI) tools for colonoscopy on the market is increasing with supporting clinical evidence. Nevertheless, their implementation is not going smoothly for a variety of reasons, including lack of data on clinical benefits and cost-effectiveness, lack of trustworthy guidelines, uncertain indications, and cost for implementation. To address this issue and better guide practitioners, the World Endoscopy Organization (WEO) has provided its perspective about the status of AI in colonoscopy as the position statement. WEO Position Statement: Statement 1.1: Computer-aided detection (CADe) for colorectal polyps is likely to improve colonoscopy effectiveness by reducing adenoma miss rates and thus increase adenoma detection; Statement 1.2: In the short term, use of CADe is likely to increase health-care costs by detecting more adenomas; Statement 1.3: In the long term, the increased cost by CADe could be balanced by savings in costs related to cancer treatment (surgery, chemotherapy, palliative care) due to CADe-related cancer prevention; Statement 1.4: Health-care delivery systems and authorities should evaluate the cost-effectiveness of CADe to support its use in clinical practice; Statement 2.1: Computer-aided diagnosis (CADx) for diminutive polyps (≤5 mm), when it has sufficient accuracy, is expected to reduce health-care costs by reducing polypectomies, pathological examinations, or both; Statement 2.2: Health-care delivery systems and authorities should evaluate the cost-effectiveness of CADx to support its use in clinical practice; Statement 3: We recommend that a broad range of high-quality cost-effectiveness research should be undertaken to understand whether AI implementation benefits populations and societies in different health-care systems.

Artificial intelligence in gastroenterology and hepatology: how to advance clinical practice while ensuring health equity.

Artificial intelligence (AI) and machine learning (ML) systems are increasingly used in medicine to improve clinical decision-making and healthcare delivery. In gastroenterology and hepatology, studies have explored a myriad of opportunities for AI/ML applications which are already making the transition to bedside. Despite these advances, there is a risk that biases and health inequities can be introduced or exacerbated by these technologies. If unrecognised, these technologies could generate or worsen systematic racial, ethnic and sex disparities when deployed on a large scale. There are several mechanisms through which AI/ML could contribute to health inequities in gastroenterology and hepatology, including diagnosis of oesophageal cancer, management of inflammatory bowel disease (IBD), liver transplantation, colorectal cancer screening and many others. This review adapts a framework for ethical AI/ML development and application to gastroenterology and hepatology such that clinical practice is advanced while minimising bias and optimising health equity.

Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial).

BACKGROUND & AIMS: Artificial intelligence-based computer-aided polyp detection (CADe) systems are intended to address the issue of missed polyps during colonoscopy. The effect of CADe during screening and surveillance colonoscopy has not previously been studied in a United States (U.S.) population. METHODS: We conducted a prospective, multi-center, single-blind randomized tandem colonoscopy study to evaluate a deep-learning based CADe system (EndoScreener, Shanghai Wision AI, China). Patients were enrolled across 4 U.S. academic medical centers from 2019 through 2020. Patients presenting for colorectal cancer screening or surveillance were randomized to CADe colonoscopy first or high-definition white light (HDWL) colonoscopy first, followed immediately by the other procedure in tandem fashion by the same endoscopist. The primary outcome was adenoma miss rate (AMR), and secondary outcomes included sessile serrated lesion (SSL) miss rate and adenomas per colonoscopy (APC). RESULTS: A total of 232 patients entered the study, with 116 patients randomized to undergo CADe colonoscopy first and 116 patients randomized to undergo HDWL colonoscopy first. After the exclusion of 9 patients, the study cohort included 223 patients. AMR was lower in the CADe-first group compared with the HDWL-first group (20.12% [34/169] vs 31.25% [45/144]; odds ratio [OR], 1.8048; 95% confidence interval [CI], 1.0780-3.0217; P = .0247). SSL miss rate was lower in the CADe-first group (7.14% [1/14]) vs the HDWL-first group (42.11% [8/19]; P = .0482). First-pass APC was higher in the CADe-first group (1.19 [standard deviation (SD), 2.03] vs 0.90 [SD, 1.55]; P = .0323). First-pass ADR was 50.44% in the CADe-first group and 43.64 % in the HDWL-first group (P = .3091). CONCLUSION: In this U.S. multicenter tandem colonoscopy randomized controlled trial, we demonstrate a decrease in AMR and SSL miss rate and an increase in first-pass APC with the use of a CADe-system when compared with HDWL colonoscopy alone.

EUS imaging for the diagnosis of nonalcoholic fatty liver disease.

BACKGROUND AND AIMS: EUS is increasingly used to evaluate patients with liver disease, but its role in assessing hepatic steatosis has not been reported. The goal of our study was to assess the accuracy of EUS for diagnosing hepatic steatosis. METHODS: We identified all patients who underwent EUS-guided liver biopsy sampling at our institution. All digitally stored EUS liver images were reviewed by a single radiologist, who rated the severity of liver echogenicity using a 4-point US scale. Liver biopsy specimens for all study patients were reviewed by a single liver pathologist, who rated them for steatosis and fibrosis using Nonalcoholic Steatohepatitis Clinical Research Network criteria. Receiver operator characteristic curves were used to assess the diagnostic accuracy of EUS for hepatic steatosis for all patients and in a subgroup analysis for obese and nonobese patients. RESULTS: During the study period, 76 patients underwent EUS-guided liver biopsy sampling. The average age of study patients was 56.5 years, 50% were women, and 43.2% were obese. The accuracy for EUS for the diagnosis of hepatic steatosis was .8 (95% confidence interval [CI], .7-.89). The accuracy of EUS for the diagnosis of hepatic steatosis in obese patients was .93 (95% CI, .8-.99) and in nonobese patients was .69 (95% CI, .54-.83). For obese patients, EUS had a positive predictive value of 89.7% and a negative predictive value of 75%. The finding of course echotexture on EUS had an accuracy of 79% for the diagnosis of grade 3 fibrosis or cirrhosis. CONCLUSIONS: EUS is a useful tool for the diagnosis of hepatic steatosis, particularly in obese patients in whom abdominal US has modest accuracy.

ERCP within 6 or 12 h for acute cholangitis: a propensity score-matched analysis.

BACKGROUND: The optimal timing of biliary drainage by endoscopic retrograde cholangiopancreatography (ERCP) for patients with acute cholangitis remains controversial. The aim of our study was to determine if ERCP performed within 6 or 12 h of presentation was associated with improved clinical outcomes. METHODS: Medical records for all patients with acute cholangitis who underwent ERCP at our institution between 2009 and 2018 were reviewed. Outcomes were compared between those who underwent ERCP within or after 12 h using propensity score framework. Our primary outcome was length of hospitalization. Secondary outcomes included in-hospital mortality, adverse events, ERCP failure, length of ICU stay, organ failure, recurrent cholangitis, and 30-day readmission. In secondary analysis, outcomes for ERCP done within or after 6 h were also compared. RESULTS: During study period, 487 patients with cholangitis were identified, of whom 147 had ERCP within 12 h of presentation. Using propensity score matching, we selected 145 pairs of patients with similar characteristics. Length of hospitalization was similar between ERCP within or after 12 h (135.9 vs 122.1 h, p 0.094). No difference was noted in mortality, ERCP failure, adverse events, need and length of ICU stay, and recurrent cholangitis. However, 30-day readmission rates were lower when ERCP within 12 h (7.6 vs 15.2, p 0.042). No significant difference was noted in aforementioned outcomes between ERCP performed within or after 6 h. CONCLUSIONS: ERCP performed within 6 h or 12 h of presentation was not associated with superior clinical outcomes, however, may result in reduced re-hospitalization.

How Would You Manage This Patient With Nonvariceal Upper Gastrointestinal Bleeding? : Grand Rounds Discussion From Beth Israel Deaconess Medical Center.

Nonvariceal upper gastrointestinal bleeding is common, morbid, and potentially fatal. Cornerstones of inpatient management include fluid resuscitation; blood transfusion; endoscopy; and initiation of proton-pump inhibitor therapy, which continues in an individualized manner based on risk factors for recurrent bleeding in the outpatient setting. The International Consensus Group released guidelines on the management of nonvariceal upper gastrointestinal bleeding in 2019. These guidelines provide a helpful, evidence-based roadmap for management of gastrointestinal bleeding but leave certain management details to the discretion of the treating physician. Here, 2 gastroenterologists consider the care of a patient with nonvariceal upper gastrointestinal bleeding from a peptic ulcer, specifically debating approaches to blood transfusion and endoscopy timing in the hospital, as well as the recommended duration of proton-pump inhibitor therapy after discharge.