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.

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.

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.

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).

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.

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.

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.).

Factors Associated With Poor Outcomes Among Patients With SARS-CoV-2 Coronavirus Infection and Gastrointestinal Symptoms.

Background and Aims: Gastrointestinal (GI) symptoms are present in 20% of patients with SARS-CoV-2 coronavirus infection (COVID-19). We studied the association of GI symptoms (in patients with COVID-19) with adverse outcomes and factors associated with poor outcomes in these patients. Methods: The study cohort included 100,902 patients from the Cerner Real-World Data COVID-19 Database of hospital encounters and emergency department visits with COVID-19 infection from December 1, 2019, to November 30, 2020. Multivariate analysis was used to study the effect of GI symptoms on adverse outcomes and the factors associated with mortality, acute respiratory distress syndrome (ARDS), sepsis, and ventilator requirement or oxygen dependence in patients with COVID-19 and GI symptoms. Results: Patients with COVID-19 and GI symptoms were significantly more likely to have ARDS (odds ratio [OR] 1.20, 95% confidence interval [CI] 1.11, 1.29), sepsis (OR 1.19, 95% CI 1.14, 1.24), acute kidney injury (OR 1.30, 95% CI 1.24, 1.36), venous thromboembolism (OR 1.36, 95% CI 1.22, 1.52), or GI bleed (OR 1.62, 95% CI 1.47, 1.79) and less likely to experience cardiomyopathy (OR 0.87, 95% CI 0.77, 0.99) or death (OR 0.71, 95% CI 0.67, 0.75). Among those with GI symptoms, older age, higher Charlson comorbidity index scores, and use of proton pump inhibitors/H2 receptor antagonists were associated with higher mortality, ARDS, sepsis, and ventilator or oxygen requirement. Conclusion: Patients with COVID-19 who have GI symptoms have overall worse in-hospital complications but less cardiomyopathy and mortality. Older age, higher comorbidity scores, and the use of proton pump inhibitors and H2 receptor antagonists are associated with poor outcomes in these patients.

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.

SinGAN-Seg: Synthetic training data generation for medical image segmentation.

Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. However, the machine learning models used to build these tools are highly dependent on the data used to train them. Large amounts of data can be difficult to obtain in medicine due to privacy reasons, expensive and time-consuming annotations, and a general lack of data samples for infrequent lesions. In this study, we present a novel synthetic data generation pipeline, called SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image. Our method is different from the traditional generative adversarial networks (GANs) because our model needs only a single image and the corresponding ground truth to train. We also show that the synthetic data generation pipeline can be used to produce alternative artificial segmentation datasets with corresponding ground truth masks when real datasets are not allowed to share. The pipeline is evaluated using qualitative and quantitative comparisons between real data and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited. By training UNet++ using both real data and the synthetic data generated from the SinGAN-Seg pipeline, we show that the models trained on synthetic data have very close performances to those trained on real data when both datasets have a considerable amount of training data. In contrast, we show that synthetic data generated from the SinGAN-Seg pipeline improves the performance of segmentation models when training datasets do not have a considerable amount of data. All experiments were performed using an open dataset and the code is publicly available on GitHub.

On evaluation metrics for medical applications of artificial intelligence.

Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model's performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of binary classification in the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research.

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.

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.

Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images.

Recent trials have evaluated the efficacy of deep convolutional neural network (CNN)-based AI systems to improve lesion detection and characterization in endoscopy. Impressive results are achieved, but many medical studies use a very small image resolution to save computing resources at the cost of losing details. Today, no conventions between resolution and performance exist, and monitoring the performance of various CNN architectures as a function of image resolution provides insights into how subtleties of different lesions on endoscopy affect performance. This can help set standards for image or video characteristics for future CNN-based models in gastrointestinal (GI) endoscopy. This study examines the performance of CNNs on the HyperKvasir dataset, consisting of 10,662 images from 23 different findings. We evaluate two CNN models for endoscopic image classification under quality distortions with image resolutions ranging from 32 × 32 to 512 × 512 pixels. The performance is evaluated using two-fold cross-validation and F1-score, maximum Matthews correlation coefficient (MCC), precision, and sensitivity as metrics. Increased performance was observed with higher image resolution for all findings in the dataset. MCC was achieved at image resolutions between 512 × 512 pixels for classification for the entire dataset after including all subclasses. The highest performance was observed with an MCC value of 0.9002 when the models were trained on the highest resolution and tested on the same resolution. Different resolutions and their effect on CNNs are explored. We show that image resolution has a clear influence on the performance which calls for standards in the field in the future.

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.

Prevalence of Gastrointestinal Symptoms and Fecal Viral Shedding in Patients With Coronavirus Disease 2019: A Systematic Review and Meta-analysis.

Importance: Coronavirus disease 2019 (COVID-19) is a global pandemic and can involve the gastrointestinal (GI) tract, including symptoms like diarrhea and shedding of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in feces. Objective: To provide a pooled estimate of GI symptoms, liver enzyme levels outside reference ranges, and fecal tests positive for SARS-CoV-2 among patients with COVID-19. Data Sources: An electronic literature search was performed for published (using MEDLINE/PubMed and Embase) and preprint (using bioRxiv and medRxiv) studies of interest conducted from November 1, 2019, to March 30, 2020. Search terms included "COVID-19," "SARS-Cov-2," and/or "novel coronavirus." Study Selection: Eligible studies were those including patients with SARS-CoV-2 infection who reported GI symptoms. Data Extraction and Synthesis: Data on patients with GI symptoms (ie, diarrhea, nausea, or vomiting), liver enzyme level changes, and fecal shedding of virus were extracted. Quality of studies was examined using methodological index for nonrandomized studies. Pooled estimates (%) were reported with 95% CIs with level of heterogeneity (I2). Main Outcomes and Measures: Study and patient characteristics with pooled detection rates for diarrhea, nausea or vomiting, liver enzyme levels outside reference ranges, and SARS-CoV-2 positivity in feces tests were analyzed. Results: Of 1484 records reviewed, 23 published and 6 preprint studies were included in the analysis, with a total of 4805 patients (mean [SD] age, 52.2 [14.8] years; 1598 [33.2%] women) with COVID-19. The pooled rates were 7.4% (95% CI, 4.3%-12.2%) of patients reporting diarrhea and 4.6% (95% CI, 2.6%-8.0%) of patients reporting nausea or vomiting. The pooled rate for aspartate aminotransferase levels outside reference ranges was 20% (95% CI, 15.3%-25.6%) of patients, and the pooled rate for alanine aminotransferase levels outside reference ranges was 14.6% (95% CI, 12.8%-16.6%) of patients. Fecal tests that were positive for SARS-CoV-2 were reported in 8 studies, and viral RNA shedding was detected in feces in 40.5% (95% CI, 27.4%-55.1%) of patients. There was high level of heterogeneity (I2 = 94%), but no statistically significant publication bias noted. Conclusions and Relevance: These findings suggest that that 12% of patients with COVID-19 will manifest GI symptoms; however, SAR-CoV-2 shedding was observed in 40.5% of patients with confirmed SARS-CoV-2 infection. This highlights the need to better understand what measures are needed to prevent further spread of this highly contagious pathogen.