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 High-Quality Upper Endoscopy: Expert Review.

DESCRIPTION: The purpose of this Clinical Practice Update (CPU) Expert Review is to provide clinicians with guidance on best practices for performing a high-quality upper endoscopic exam. METHODS: The best practice advice statements presented herein were developed from a combination of available evidence from published literature, guidelines, and consensus-based expert opinion. No formal rating of the strength or quality of the evidence was carried out, which aligns with standard processes for American Gastroenterological Association (AGA) Institute CPUs. These statements are meant to provide practical, timely advice to clinicians practicing in the United States. This Expert Review was commissioned and approved by the American Gastroenterological Association (AGA) Institute Clinical Practice Updates (CPU) Committee 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 CPU Committee and external peer review through standard procedures of Clinical Gastroenterology & Hepatology. BEST PRACTICE ADVICE 1: Endoscopists should ensure that upper endoscopy is being performed for an appropriate indication and that informed consent clearly explaining the risks, benefits, alternatives, sedation plan, and potential diagnostic and therapeutic interventions is obtained. These elements should be documented by the endoscopist before the procedure. BEST PRACTICE ADVICE 2: Endoscopists should ensure that adequate visualization of the upper gastrointestinal mucosa, using mucosal cleansing and insufflation as necessary, is achieved and documented. BEST PRACTICE ADVICE 3: A high-definition white-light endoscopy system should be used for upper endoscopy instead of a standard-definition white-light endoscopy system whenever possible. The endoscope used for the procedure should be documented in the procedure note. BEST PRACTICE ADVICE 4: Image enhancement technologies should be used during the upper endoscopic examination to improve the diagnostic yield for preneoplasia and neoplasia. Suspicious areas should be clearly described, photodocumented, and biopsied separately. BEST PRACTICE ADVICE 5: Endoscopists should spend sufficient time carefully inspecting the foregut mucosa in an anterograde and retroflexed view to improve the detection and characterization of abnormalities. BEST PRACTICE ADVICE 6: Endoscopists should document any abnormalities noted on upper endoscopy using established classifications and standard terminology whenever possible. BEST PRACTICE ADVICE 7: Endoscopists should perform biopsies for the evaluation and management of foregut conditions using standardized biopsy protocols. BEST PRACTICE ADVICE 8: Endoscopists should provide patients with management recommendations based on the specific endoscopic findings (eg, peptic ulcer disease, erosive esophagitis), and this should be documented in the medical record. If recommendations are contingent upon histopathology results (eg, H pylori infection, Barrett's esophagus), then endoscopists should document that appropriate guidance will be provided after results are available. BEST PRACTICE ADVICE 9: Endoscopists should document whether subsequent surveillance endoscopy is indicated and, if so, provide appropriate surveillance intervals. If the determination of surveillance is contingent on histopathology results, then endoscopists should document that surveillance intervals will be suggested after results are available.

Use of a Novel Artificial Intelligence System Leads to the Detection of Significantly Higher Number of Adenomas During Screening and Surveillance Colonoscopy: Results From a Large, Prospective, US Multicenter, Randomized Clinical Trial.

INTRODUCTION: Adenoma per colonoscopy (APC) has recently been proposed as a quality measure for colonoscopy. We evaluated the impact of a novel artificial intelligence (AI) system, compared with standard high-definition colonoscopy, for APC measurement. METHODS: This was a US-based, multicenter, prospective randomized trial examining a novel AI detection system (EW10-EC02) that enables a real-time colorectal polyp detection enabled with the colonoscope (CAD-EYE). Eligible average-risk subjects (45 years or older) undergoing screening or surveillance colonoscopy were randomized to undergo either CAD-EYE-assisted colonoscopy (CAC) or conventional colonoscopy (CC). Modified intention-to-treat analysis was performed for all patients who completed colonoscopy with the primary outcome of APC. Secondary outcomes included positive predictive value (total number of adenomas divided by total polyps removed) and adenoma detection rate. RESULTS: In modified intention-to-treat analysis, of 1,031 subjects (age: 59.1 ± 9.8 years; 49.9% male), 510 underwent CAC vs 523 underwent CC with no significant differences in age, gender, ethnicity, or colonoscopy indication between the 2 groups. CAC led to a significantly higher APC compared with CC: 0.99 ± 1.6 vs 0.85 ± 1.5, P = 0.02, incidence rate ratio 1.17 (1.03-1.33, P = 0.02) with no significant difference in the withdrawal time: 11.28 ± 4.59 minutes vs 10.8 ± 4.81 minutes; P = 0.11 between the 2 groups. Difference in positive predictive value of a polyp being an adenoma among CAC and CC was less than 10% threshold established: 48.6% vs 54%, 95% CI -9.56% to -1.48%. There were no significant differences in adenoma detection rate (46.9% vs 42.8%), advanced adenoma (6.5% vs 6.3%), sessile serrated lesion detection rate (12.9% vs 10.1%), and polyp detection rate (63.9% vs 59.3%) between the 2 groups. There was a higher polyp per colonoscopy with CAC compared with CC: 1.68 ± 2.1 vs 1.33 ± 1.8 (incidence rate ratio 1.27; 1.15-1.4; P < 0.01). DISCUSSION: Use of a novel AI detection system showed to a significantly higher number of adenomas per colonoscopy compared with conventional high-definition colonoscopy without any increase in colonoscopy withdrawal time, thus supporting the use of AI-assisted colonoscopy to improve colonoscopy quality ( ClinicalTrials.gov NCT04979962).

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.

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.

Impact of withdrawal time on adenoma detection rate: results from a prospective multicenter trial.

BACKGROUND AND AIMS: Performing a high-quality colonoscopy is critical for optimizing the adenoma detection rate (ADR). Colonoscopy withdrawal time (a surrogate measure) of ≥6 minutes is recommended; however, a threshold of a high-quality withdrawal and its impact on ADR are not known. METHODS: We examined withdrawal time (excluding polyp resection and bowel cleaning time) of subjects undergoing screening and/or surveillance colonoscopy in a prospective, multicenter, randomized controlled trial. We examined the relationship of withdrawal time in 1-minute increments on ADR and reported odds ratio (OR) with 95% confidence intervals. Linear regression analysis was performed to assess the maximal inspection time threshold that impacts the ADR. RESULTS: A total of 1142 subjects (age, 62.3 ± 8.9 years; 80.5% men) underwent screening (45.9%) or surveillance (53.6%) colonoscopy. The screening group had a median withdrawal time of 9.0 minutes (interquartile range [IQR], 3.3) with an ADR of 49.6%, whereas the surveillance group had a median withdrawal time of 9.3 minutes (IQR, 4.3) with an ADR of 63.9%. ADR correspondingly increased for a withdrawal time of 6 minutes to 13 minutes, beyond which ADR did not increase (50.4% vs 76.6%, P < .01). For every 1-minute increase in withdrawal time, there was 6% higher odds of detecting an additional subject with an adenoma (OR, 1.06; 95% confidence interval, 1.02-1.10; P = .004). CONCLUSIONS: Results from this multicenter, randomized controlled trial underscore the importance of a high-quality examination and efforts required to achieve this with an incremental yield in ADR based on withdrawal time. (Clinical trial registration number: NCT03952611.).

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.

Cholecystectomy for Biliary Gallstone Pancreatitis Is Often Not Performed During Index Hospitalization and Is Associated With Worse Outcomes.

Guidelines recommend that patients with mild gallstone pancreatitis (GSP) without necrosis or infection should undergo cholecystectomy during the index hospitalization before discharge.1,2 However, in routine clinical practice, cholecystectomy is often performed several weeks after hospital discharge, or not performed at all.3.

High-Definition Colonoscopy Compared With Cuff- and Cap-Assisted Colonoscopy: Results From a Multicenter, Prospective, Randomized Controlled Trial.

BACKGROUND AND AIMS: Mucosal exposure devices including distal attachments such as the cuff and cap have shown variable results in improving adenoma detection rate (ADR) compared with high-definition white light colonoscopy (HDWLE). METHODS: We performed a prospective, multicenter randomized controlled trial in patients undergoing screening or surveillance colonoscopy comparing HDWLE to 2 different types of distal attachments: cuff (CF) (Endocuff Vision) or cap (CP) (Reveal). The primary outcome was ADR. Secondary outcomes included adenomas per colonoscopy, advanced adenoma and sessile serrated lesion detection rate, right-sided ADR, withdrawal time, and adverse events. Continuous variables were compared using Student's t test and categorical variables were compared using chi-square or Fisher's exact test using statistical software Stata version16. A P value <.05 was considered significant. RESULTS: A total of 1203 subjects were randomized to either HDWLE (n = 384; mean 62 years of age; 81.3% males), CF (n = 379; mean 62.7 years of age; 79.9% males) or CP (n = 379; mean age 62.1 years of age; 80.5% males). No significant differences were found among 3 groups for ADR (57.3%, 59.1%, and 55.7%; P = .6), adenomas per colonoscopy (1.4 ± 1.9, 1.6 ± 2.4, and 1.4 ± 2; P = .3), advanced adenoma (7.6%, 9.2%, and 8.2%; P = .7), sessile serrated lesion (6.8%, 6.3%, and 5.5%; P = .8), or right ADR (48.2%, 49.3%, and 46.2%; P = .7). The number of polyps per colonoscopy were significantly higher in the CF group compared with HDWLE and CP group (2.7 ± 3.4, 2.3 ± 2.5, and 2.2 ± 2.3; P = .013). In a multivariable model, after adjusting for age, sex, body mass index, withdrawal time, and Boston Bowel Preparation Scale score, there was no impact of device type on the primary outcome of ADR (P = .77). In screening patients, CF resulted in more neoplasms per colonoscopy (CF: 1.7 ± 2.6, HDWLE: 1.3 ± 1.7, and CP: 1.2 ± 1.8; P = .047) with a shorter withdrawal time. CONCLUSIONS: Results from this multicenter randomized controlled trial do not show any significant benefit of using either distal attachment devices (CF or CP) over HDWLE, at least in high-detector endoscopists. The Endocuff may have an advantage in the screening population. (ClinicalTrials.gov, Number: NCT03952611).

Educational intervention to improve quality of care in Barrett's esophagus: the AQUIRE randomized controlled trial.

BACKGROUND AND AIMS: Despite quality measures in upper endoscopy (EGD) for Barrett's esophagus (BE), considerable variability remains in practice among gastroenterologists. This randomized controlled trial evaluated the role of structured intensive training on the quality of EGD in BE. METHODS: In this multicenter study, 8 sites (from the GI Quality Consortium) were cluster randomized (1:1) to receive AQUIRE (A Quality Improvement program in cancer care during Endoscopy) training (intervention) or continue local standard practices (control). The primary outcome was compliance with the Seattle biopsy protocol. Secondary outcomes were change in knowledge of BE detection and sampling assessed by questionnaire and dysplasia detection rate (DDR) before and after completion of the 6-month study period. RESULTS: The intervention sites (n = 4) had 31 gastroenterologists and the control sites (n = 4) had 34. There was a significant improvement in the compliance rates with the Seattle biopsy protocol from baseline to the end of the study in the intervention sites (64.8%-73.2%, P = .002) but not in the control sites (69.5%-69.4%, P = .953). The accurate response rate on the questionnaire at the intervention sites increased from 73% at baseline to 88% after AQUIRE training (difference, 14.8%; standard deviation, 18.7; P = .008). DDR did not change significantly from baseline to 6 months in either the control or intervention groups (P = .06). CONCLUSIONS: This study confirms the capacity of a structured educational intervention to improve utilization of a standard biopsy protocol and knowledge of standards of care in BE but without significant change in DDR.

Impact of age, sex, race, and regionality on major clinical outcomes of COVID-19 in hospitalized patients in the United States.

BACKGROUND: The COVID-19 pandemic has affected all people across the globe. Regional and community differences in timing and severity of surges throughout the pandemic can provide insight into risk factors for worse outcomes in those hospitalized with COVID-19. METHODS: The study cohort was derived from the Cerner Real World Data (CRWD) COVID-19 Database made up of hospitalized patients with proven infection from December 1, 2019 through November 30, 2020. Baseline demographic information, comorbidities, and hospital characteristics were obtained. We performed multivariate analysis to determine if age, race, comorbidity and regionality were predictors for mortality, ARDS, mechanical ventilation or sepsis hospitalized patients with COVID-19. RESULTS: Of 100,902 hospitalized COVID-19 patients included in the analysis (median age 52 years, IQR 36-67; 50.7% female), COVID-19 case fatality rate was 8.5% with majority of deaths in those ≥ 65 years (70.8%). In multivariate analysis, age ≥ 65 years, male gender and higher Charlson Comorbidity Index (CCI) were independent risk factors for mortality and ARDS. Those identifying as non-Black or non-White race have a marginally higher risk for mortality (OR 1.101, CI 1.032-1.174) and greater risk of ARDS (OR 1.44, CI 1.334-1.554) when compared to those who identify as White. The risk of mortality or ARDS was similar for Blacks as Whites. Multivariate analysis found higher mortality risk in the Northeast (OR 1.299, CI 1.22-1.29) and West (OR 1.26, CI 1.18-1.34). Larger hospitals also had an increased risk of mortality, greatest in hospitals with 500-999 beds (OR 1.67, CI 1.43-1.95). CONCLUSION: Advanced age, male sex and a higher CCI predicted worse outcomes in hospitalized COVID-19 patients. In multivariate analysis, worse outcomes were identified in small minority populations, however there was no difference in study outcomes between those who identify as Black or White.

Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force.

Artificial intelligence (AI) in GI endoscopy holds tremendous promise to augment clinical performance, establish better treatment plans, and improve patient outcomes. Although there are promising initial applications and preliminary clinical data for AI in gastroenterology, the field is still in a very early phase, with limited clinical use. The American Society for Gastrointestinal Endoscopy has convened an AI Task Force to develop guidance around clinical implementation, testing/validating algorithms, and building pathways for successful implementation of AI in GI endoscopy. This White Paper focuses on 3 areas: (1) priority use cases for development of AI algorithms in GI, both for specific clinical scenarios and for streamlining clinical workflows, quality reporting, and practice management; (2) data science priorities, including development of image libraries, and standardization of methods for storing, sharing, and annotating endoscopic images/video; and (3) research priorities, focusing on the importance of high-quality, prospective trials measuring clinically meaningful patient outcomes.

Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit.

BACKGROUND AND AIMS: Artificial intelligence (AI), specifically deep learning, offers the potential to enhance the field of GI endoscopy in areas ranging from lesion detection and classification to quality metrics and documentation. Progress in this field will be measured by whether AI implementation can lead to improved patient outcomes and more efficient clinical workflow for GI endoscopists. The aims of this article are to report the findings of a multidisciplinary group of experts focusing on issues in AI research and applications related to gastroenterology and endoscopy, to review the current status of the field, and to produce recommendations for investigators developing and studying new AI technologies for gastroenterology. METHODS: A multidisciplinary meeting was held on September 28, 2019, bringing together academic, industry, and regulatory experts in diverse fields including gastroenterology, computer and imaging sciences, machine learning, computer vision, U.S. Food and Drug Administration, and the National Institutes of Health. Recent and ongoing studies in gastroenterology and current technology in AI were presented and discussed, key gaps in knowledge were identified, and recommendations were made for research that would have the highest impact in making advances and implementation in the field of AI to gastroenterology. RESULTS: There was a consensus that AI will transform the field of gastroenterology, particularly endoscopy and image interpretation. Powered by advanced machine learning algorithms, the use of computer vision in endoscopy has the potential to result in better prediction and treatment outcomes for patients with gastroenterology disorders and cancer. Large libraries of endoscopic images, "EndoNet," will be important to facilitate development and application of AI systems. The regulatory environment for implementation of AI systems is evolving, but common outcomes such as colon polyp detection have been highlighted as potential clinical trial endpoints. Other threshold outcomes will be important, as well as clarity on iterative improvement of clinical systems. CONCLUSIONS: Gastroenterology is a prime candidate for early adoption of AI. AI is rapidly moving from an experimental phase to a clinical implementation phase in gastroenterology. It is anticipated that the implementation of AI in gastroenterology over the next decade will have a significant and positive impact on patient care and clinical workflows. Ongoing collaboration among gastroenterologists, industry experts, and regulatory agencies will be important to ensure that progress is rapid and clinically meaningful. However, several constraints and areas will benefit from further exploration, including potential clinical applications, implementation, structure and governance, role of gastroenterologists, and potential impact of AI in gastroenterology.

Estimating neoplasia detection rate (NDR) in patients with Barrett's oesophagus based on index endoscopy: a systematic review and meta-analysis.

BACKGROUND: Biopsies are obtained to confirm intestinal metaplasia and rule out prevalent dysplasia and cancer when Barrett's oesophagus (BE) is detected at index upper endoscopy (oesophagogastroduodenoscopy [EGD]). AIM: The purpose of this systematic review was to obtain summary estimates of the prevalence of high-grade dysplasia (HGD) and oesophageal adenocarcinoma (EAC) associated with BE during index EGD for chronic GERD symptoms, defined as neoplasia detection rate (NDR) which could be used as a quality measure. METHODS: An extensive search was performed within PUBMED, EMBASE and the Cochrane Library databases to identify studies in which patients underwent index endoscopy for the evaluation of the presence of BE. Two reviewers independently evaluated both the study eligibility and methodological quality and data extraction. A random-effects model (REM) based on the binomial distribution was used to calculate the pooled effects of the prevalence of BE-associated dysplasia and EAC. RESULTS: For the calculation of dysplasia and EAC prevalence rates, a total of 11 studies with 10 632 patients met the inclusion criteria including 80.4% men with a mean age of 58.7 years and average BE length of 3.5 cm. The pooled prevalence of EAC, HGD and LGD was 3%(95% CI 2 to 5, 9 studies: 396/10 539 patients), 3%(95% CI 2 to 5 [REM], 9 studies: 388/10 539 patients) and 10%(95% CI 7 to 15 [REM], 10 studies: 907/8945 patients), respectively. For NDR, that is, the pooled prevalence of HGD/EAC was 7%(95% CI 4 to 10 [REM], 10 studies: 795/10 632 patients). CONCLUSION: NDR is approximately 4% and could be used as a quality measure.

Development and Validation of a Model to Determine Risk of Progression of Barrett's Esophagus to Neoplasia.

BACKGROUND & AIMS: A system is needed to determine the risk of patients with Barrett's esophagus for progression to high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC). We developed and validated a model to determine of progression to HGD or EAC in patients with BE, based on demographic data and endoscopic and histologic findings at the time of index endoscopy. METHODS: We performed a longitudinal study of patients with BE at 5 centers in United States and 1 center in Netherlands enrolled in the Barrett's Esophagus Study database from 1985 through 2014. Patients were excluded from the analysis if they had less than 1 year of follow-up, were diagnosed with HGD or EAC within the past year, were missing baseline histologic data, or had no intestinal metaplasia. Seventy percent of the patients were used to derive the model and 30% were used for the validation study. The primary outcome was development of HGD or EAC during the follow-up period (median, 5.9 years). Survival analysis was performed using the Kaplan-Meier method. We assigned a specific number of points to each BE risk factor, and point totals (scores) were used to create categories of low, intermediate, and high risk. We used Cox regression to compute hazard ratios and 95% confidence intervals to determine associations between risk of progression and scores. RESULTS: Of 4584 patients in the database, 2697 were included in our analysis (84.1% men; 87.6% Caucasian; mean age, 55.4 ± 20.1 years; mean body mass index, 27.9 ± 5.5 kg/m2; mean length of BE, 3.7 ± 3.2 cm). During the follow-up period, 154 patients (5.7%) developed HGD or EAC, with an annual rate of progression of 0.95%. Male sex, smoking, length of BE, and baseline-confirmed low-grade dysplasia were significantly associated with progression. Scores assigned identified patients with BE that progressed to HGD or EAC with a c-statistic of 0.76 (95% confidence interval, 0.72-0.80; P < .001). The calibration slope was 0.9966 (P = .99), determined from the validation cohort. CONCLUSIONS: We developed a scoring system (Progression in Barrett's Esophagus score) based on male sex, smoking, length of BE, and baseline low-grade dysplasia that identified patients with BE at low, intermediate, and high risk for HGD or EAC. This scoring system might be used in management of patients.

Lower Annual Rate of Progression of Short-Segment vs Long-Segment Barrett's Esophagus to Esophageal Adenocarcinoma.

BACKGROUND & AIMS: European guidelines recommend different surveillance intervals of non-dysplastic Barrett's esophagus (NDBE) based on segment length, as opposed to guidelines in the United States, which do recommend surveillance intervals based on BE length. We studied rates of progression of NDBE to high-grade dysplasia (HGD) or esophageal adenocarcinoma (EAC) in patients with short-segment BE using the definition of BE in the latest guidelines (length ≥1 cm). METHODS: We collected demographic, clinical, endoscopy, and histopathology data from 1883 patients with endoscopic evidence of NDBE (mean age, 57.3 years; 83.5% male; 88.1% Caucasians) seen at 7 tertiary referral centers. Patients were followed for a median 6.4 years. Cases of dysplasia or EAC detected within 1 year of index endoscopy were considered prevalent and were excluded. Unadjusted rates of progression to HGD or EAC were compared between patients with short (≥1 and <3) and long (≥3) BE lengths using log-rank tests. A subgroup analysis was performed on patients with a documented Prague C&M classification. We used a multivariable proportional hazards model to evaluate the association between BE length and progression. Adjusted hazards ratios were calculated after adjusting for variables associated with progression. RESULTS: We found 822 patients to have a short-segment BE (SSBE) and 1061 to have long segment BE (LSBE). We found patients with SSBE to have a significantly lower annual rate of progression to EAC (0.07%) than of patients with LSBE (0.25%) (P = .001). For the combined endpoint of HGD or EAC, annual progression rates were significantly lower among patients with SSBE (0.29%) compared to compared to LSBE (0.91%) (P < .001). This effect persisted in multivariable analysis (hazard ratio, 0.32; 95% CI, 0.18-0.57; P < .001). CONCLUSION: We analyzed progression of BE (length ≥1 cm) to HGD or EAC in a large cohort of patients seen at multiple centers and followed for a median 6.4 years. We found a lower annual rate of progression of SSBE to EAC (0.07%/year) than of LSBE (0.25%/year). We propose lengthening current surveillance intervals for patients with SSBE.

Development and validation of a web-based electronic application in managing antithrombotic agents in patients undergoing GI endoscopy.

BACKGROUND AND AIMS: Antithrombotic therapy among patients undergoing GI procedures is frequently encountered and can impact the procedure and patient outcomes. The American Society for Gastrointestinal Endoscopy (ASGE) guidelines help to manage these medications before endoscopy depending on the patient's clinical status and the type of GI procedure. However, currently there is no readily available electronic tool that can assist in decision-making regarding preprocedural management of these agents. Our aim was to develop an electronic application, endoscopy + aid (ENDOAID), to help manage antithrombotic agents before endoscopy and to perform a validation study to test its accuracy. METHODS: ENDOAID, a web-based application, was developed using JavaScript software (Oracle Corporation, Redwood Shores, Calif, USA) based on an algorithm to categorize patients and procedures into low and high risk as outlined in the updated ASGE guidelines published in 2016. Once pertinent information regarding a patient's clinical status and the procedure are entered, the application generates recommendations for the management of antithrombotic agents based on their cardiovascular risk and published ASGE guidelines. We performed a validation study with 52 patients who were referred to endoscopy and were taking antithrombotic agents. The patients were divided into groups of 5, and in the simulation each patient had 4 procedures. Different GI procedures were used in the simulation for each group of patients to ensure the entire spectrum of procedures were covered for analysis. Every simulation was then run through ENDOAID. The results from ENDOAID were compared with recommendations based on ASGE guidelines. The latter was derived by consensus between 2 endoscopists (the criterion standard). The personnel using the ENDOAID and those using the ASGE guidelines were different to avoid bias. Any clinical scenario that was unclear or not clearly outlined in ASGE guidelines was discussed with expert endoscopists for a final decision. We evaluated ASGE recommendations and calculated concordance rates between guidelines and ENDOAID results. The Pearson correlation coefficient (r) was calculated to assess the correlation between ENDOAID results to guidelines. RESULTS: There was a total of 208 simulated encounters, including 26 procedures. Initial concordance between ENDOAID recommendations and the criterion standard was seen in 206 encounters (99.03%). The 2 encounters that needed further review occurred among patients with Factor V Leiden mutation and deep vein thrombosis from antiphospholipid antibody syndrome and who were undergoing high-risk procedures that had ambiguous guidelines. ENDOAID suggested consultations with an expert before the elective procedure. This suggestion was agreed on by expert endoscopist consensus. Thus, ENDOAID showed a 100% concordance with the ASGE guideline for managing antithrombotics. There was a high degree of correlation (r = .996, P < .01) between ENDOAID results with ASGE. CONCLUSIONS: We have developed and validated an easy-to-use web-based application that can help in periprocedural management of antithrombotics. Such an application has the potential to simplify the management of these agents and potentially prevent procedural delays, cancellations, or unnecessary consults.

Increasing prevalence of high-grade dysplasia and adenocarcinoma on index endoscopy in Barrett's esophagus over the past 2 decades: data from a multicenter U.S. consortium.

INTRODUCTION: Data on time trends of dysplasia and esophageal adenocarcinoma (EAC) in Barrett's esophagus (BE) during the index endoscopy (ie, prevalent cases) are limited. Our aim was to determine the prevalence patterns of BE-associated dysplasia on index endoscopy over the past 25 years. METHODS: The Barrett's Esophagus Study is a multicenter outcome project of a large cohort of patients with BE. Proportions of patients with index endoscopy findings of no dysplasia (NDBE), low-grade dysplasia (LGD), high-grade dysplasia (HGD), and EAC were extracted per year of index endoscopy, and 5-yearly patient cohorts were tabulated over years 1990 to 2010+ (2010-current). Prevalent dysplasia and endoscopic findings were trended over the past 25 years using percentage dysplasia (LGD, HGD, EAC, and HGD/EAC) to assess changes in detection of BE-associated dysplasia over the last 25 years. Statistical analysis was done using SAS version 9.4 software (SAS, Cary, NC). RESULTS: A total of 3643 patients were included in the analysis with index endoscopy showing NDBE in 2513 (70.1%), LGD in 412 (11.5%), HGD in 193 (5.4%), and EAC in 181 (5.1%). Over time, there was an increase in the mean age of patients with BE (51.7 ± 29 years vs 62.6 ± 11.3 years) and the proportion of males (84% vs 92.6%) diagnosed with BE but a decrease in the mean BE length (4.4±4.3 cm vs 2.9±3.0 cm) as time progressed (1990-1994 to 2010-2016 time periods). The presence of LGD on index endoscopy remained stable over 1990 to 2016. However, a significant increase (148% in HGD and 112% in EAC) in the diagnosis of HGD, EAC, and HGD/EAC was noted on index endoscopy over the last 25 years (P < .001). There was also a significant increase in the detection of visible lesions on index endoscopy (1990-1994, 5.1%; to 2005-2009, 6.3%; and 2010+, 16.3%) during the same period. CONCLUSION: Our results suggest that the prevalence of HGD and EAC has significantly increased over the past 25 years despite a decrease in BE length during the same period. This increase parallels an increase in the detection of visible lesions, suggesting that a careful examination at the index examination is crucial.

Epidemiology of Colorectal Cancer in Average Risk Adults 20-39 Years of Age: A Population-Based National Study.

BACKGROUND/AIMS: While overall rates of colorectal cancer (CRC) have declined in individuals aged above 50 years of age, this decline has not been seen in younger individuals who do not benefit from current screening guidelines. We sought to describe the prevalence of CRC in adults 20-39 years of age without family history of CRC or inflammatory bowel disease as early-onset CRC (EoCRC), evaluate associated signs and symptoms and medical comorbidities in EoCRC, and compare them with individuals aged 20-39 years without CRC (NoCRC). Our secondary aim was to compare EoCRC with individuals aged 40 years and above with CRC (LoCRC). METHODS: Utilizing a commercial database (Explorys Inc, Cleveland, OH), we identified a cohort of patients aged 20-39 years with first ever diagnosis of CRC between 2013 and 2018 based on the Systematized Nomenclature of Medicine-Clinical Terms. We calculated the overall prevalence rate of EoCRC, described age, race, and gender-based prevalence rates of EoCRC, and identified associated symptoms and medical comorbidities associated with EoCRC. RESULTS: The overall rate of EoCRC was 18.9/100,000. Compared to NoCRC, EoCRC patients were more likely to be Caucasian and female, with predominant symptoms of hematochezia, anemia, and decreased appetite. EoCRC group had higher prevalence rates of medical comorbidities such as diabetes, smoking, and obesity. Compared to LoCRC, EoCRC group presented more frequently with left-sided CRC and rectal cancers. CONCLUSION: This is one of the largest studies to date to describe the epidemiology of EoCRC in USA. We found EoCRC to occur predominantly in the Caucasian and female population. EoCRC presented more frequently with left-sided and rectal CRC. We also identified signs/symptoms as well as comorbidities associated with EoCRC. Patients with these features may benefit from earlier screening.