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.

Prevalence and Predictors of Barrett's Esophagus After Negative Initial Endoscopy: Analysis From Two National Databases.

BACKGROUND & AIMS: Guidelines suggest a single screening esophagogastroduodenoscopy (EGD) in patients with multiple risk factors for Barrett's esophagus (BE). We aimed to determine BE prevalence and predictors on repeat EGD after a negative initial EGD, using 2 large national databases (GI Quality Improvement Consortium [GIQuIC] and TriNetX). METHODS: Patients who underwent at least 2 EGDs were included and those with BE or esophageal adenocarcinoma detected at initial EGD were excluded. Patient demographics and prevalence of BE on repeat EGD were collected. Multivariate logistic regression was performed to assess for independent risk factors for BE detected on the repeat EGD. RESULTS: In 214,318 and 153,445 patients undergoing at least 2 EGDs over a median follow-up of 28-35 months, the prevalence of BE on repeat EGD was 1.7% in GIQuIC and 3.4% in TriNetX, respectively (26%-45% of baseline BE prevalence). Most (89%) patients had nondysplastic BE. The prevalence of BE remained stable over time (from 1 to >5 years from negative initial EGD) but increased with increasing number of risk factors. BE prevalence in a high-risk population (gastroesophageal reflux disease plus ≥1 risk factor for BE) was 3%-4%. CONCLUSIONS: In this study of >350,000 patients, rates of BE on repeat EGD ranged from 1.7%-3.4%, and were higher in those with multiple risk factors. Most were likely missed at initial evaluation, underscoring the importance of a high-quality initial endoscopic examination. Although routine repeat endoscopic BE screening after a negative initial examination is not recommended, repeat screening may be considered in carefully selected patients with gastroesophageal reflux disease and ≥2 risk factors for BE, potentially using nonendoscopic tools.

Population Based Time Trends in the Epidemiology and Mortality of Gastroesophageal Junction and Esophageal Adenocarcinoma.

BACKGROUND: Limited data are available on the epidemiology of gastroesophageal junction adenocarcinoma (GEJAC), particularly in comparison to esophageal adenocarcinoma (EAC). With the advent of molecular non-endoscopic Barrett's esophagus (BE) detection tests which sample the esophagus and gastroesophageal junction, early detection of EAC and GEJAC has become a possibility and their epidemiology has gained importance. AIMS: We sought to evaluate time trends in the epidemiology and survival of patients with EAC and GEJAC in a population-based cohort. METHODS: EAC and GEJAC patients from 1976 to 2019 were identified using ICD 9 and 10 diagnostic codes from the Rochester Epidemiology Project (REP). Clinical data and survival status were abstracted. Poisson regression was used to calculate incidence rate ratios (IRR). Survival analysis and Cox proportional models were used to assess predictors of survival. RESULTS: We included 443 patients (287 EAC,156 GEJAC). The incidence of EAC and GEJAC during 1976-2019 was 1.40 (CI 1.1-1.74) and 0.83 (CI 0.61-1.11) per 100,000 people, respectively. There was an increase in the incidence of EAC (IRR = 2.45, p = 0.011) and GEJAC (IRR = 3.17, p = 0.08) from 2000 to 2004 compared to 1995-1999, plateauing in later time periods. Most patients had associated BE and presented at advanced stages, leading to high 5-year mortality rates (66% in EAC and 59% in GEJAC). Age and stage at diagnosis were predictors of mortality. CONCLUSION: The rising incidence of EAC/GEJAC appears to have plateaued somewhat in the last decade. However, both cancers present at advanced stages with persistently poor survival, underscoring the need for early detection.

A Novel Blood-Sensing Capsule for Rapid Detection of Upper Gastrointestinal Bleeding: A Prospective Clinical Trial.

BACKGROUND & AIMS: Upper gastrointestinal bleeding (UGIB) is a common medical emergency associated with high resource utilization, morbidity, and mortality. Timely esophagogastroduodenoscopy (EGD) can be challenging from personnel, resource, and access perspectives. PillSense (EnteraSense Ltd, Galway) is a novel swallowed bleeding sensor for detection of UGIB, anticipated to aid in patient triage and guide clinical decision-making for individuals with suspected UGIB. METHODS: This is a prospective open-label, single-arm comparative clinical trial of a novel bleeding sensor for patients with suspected UGIB performed at a tertiary care center. The PillSense System consists of an optical sensor and an external receiver that processes and displays data from the capsule as "Blood Detected" or "No Blood Detected". Patients underwent EGD within 4 hours of capsule administration; participants were followed up to 21 days to confirm capsule passage. RESULTS: 126 patients were accrued to the study (59.5% males, mean age 62.4 ± 14.3 years). Sensitivity and specificity for detecting the presence of blood were 92.9% (p = 0.02) and 90.6% (p < 0.001), respectively. The capsule's positive and negative predictive values were 74.3% and 97.8%., respectively, and positive and negative likelihood ratios were 9.9 and 0.08, respectively. No adverse events or deaths occurred related to the PillSense System, and all capsules were excreted from patients on follow-up. CONCLUSIONS: The PillSense System is safe and effective for detecting the presence of blood in patients evaluated for UGIB prior to upper GI endoscopy. It is a rapidly deployed tool, with easy-to-interpret results that will impact the diagnosis and triage of patients with suspected UGIB. (ClinicalTrials.gov, NCT05385224).

Clinical Outcomes After Endoscopic Management of Low-Risk and High-Risk T1a Esophageal Adenocarcinoma: A Multicenter Study.

INTRODUCTION: Endoscopic eradication therapy (EET) is standard of care for T1a esophageal adenocarcinoma (EAC). However, data on outcomes in high-risk T1a EAC are limited. We assessed and compared outcomes after EET of low-risk and high-risk T1a EAC, including intraluminal EAC recurrence, extraesophageal metastases, and overall survival. METHODS: Patients who underwent EET for T1a EAC at 3 referral Barrett's esophagus endotherapy units between 1996 and 2022 were included. Patients with submucosal invasion, positive deep margins, or metastases at initial diagnosis were excluded. High-risk T1a EAC was defined as T1a EAC with poor differentiation and/or lymphovascular invasion, with low-risk disease being defined without these features. All pathology was systematically assessed by expert gastrointestinal pathologists. Baseline and follow-up endoscopy and pathology data were abstracted. Time-to-event analyses were performed to compare outcomes between groups. RESULTS: One hundred eighty-eight patients with T1a EAC were included (high risk, n = 45; low risk, n = 143) with a median age of 70 years, and 84% were men. Groups were comparable for age, sex, Barrett's esophagus length, lesion size, and EET technique. Rates of delayed extraesophageal metastases (11.1% vs 1.4%) were significantly higher in the high-risk group ( P = 0.02). There was no significant difference in the rates of intraluminal EAC recurrence ( P = 0.79) and overall survival ( P = 0.73) between the 2 groups. DISCUSSION: Patients with high-risk T1a EAC undergoing successful EET had a substantially higher rate of extraesophageal metastases compared with those with low-risk T1a EAC on long-term follow-up. These data should be factored into discussions with patients while selecting treatment approaches. Additional prospective data in this area are critical.

Development of Electronic Health Record-Based Machine Learning Models to Predict Barrett's Esophagus and Esophageal Adenocarcinoma Risk.

INTRODUCTION: Screening for Barrett's esophagus (BE) is suggested in those with risk factors, but remains underutilized. BE/esophageal adenocarcinoma (EAC) risk prediction tools integrating multiple risk factors have been described. However, accuracy remains modest (area under the receiver-operating curve [AUROC] ≤0.7), and clinical implementation has been challenging. We aimed to develop machine learning (ML) BE/EAC risk prediction models from an electronic health record (EHR) database. METHODS: The Clinical Data Analytics Platform, a deidentified EHR database of 6 million Mayo Clinic patients, was used to predict BE and EAC risk. BE and EAC cases and controls were identified using International Classification of Diseases codes and augmented curation (natural language processing) techniques applied to clinical, endoscopy, laboratory, and pathology notes. Cases were propensity score matched to 5 independent randomly selected control groups. An ensemble transformer-based ML model architecture was used to develop predictive models. RESULTS: We identified 8,476 BE cases, 1,539 EAC cases, and 252,276 controls. The BE ML transformer model had an overall sensitivity, specificity, and AUROC of 76%, 76%, and 0.84, respectively. The EAC ML transformer model had an overall sensitivity, specificity, and AUROC of 84%, 70%, and 0.84, respectively. Predictors of BE and EAC included conventional risk factors and additional novel factors, such as coronary artery disease, serum triglycerides, and electrolytes. DISCUSSION: ML models developed on an EHR database can predict incident BE and EAC risk with improved accuracy compared with conventional risk factor-based risk scores. Such a model may enable effective implementation of a minimally invasive screening technology.

Long-term outcomes following successful endoscopic treatment of T1 esophageal adenocarcinoma: a multicenter cohort study.

BACKGROUND AND AIMS: Endoscopic eradication therapy (EET) is guideline endorsed for management of early-stage (T1) esophageal adenocarcinoma (EAC). Patients with baseline high-grade dysplasia (HGD) and EAC are at highest risk of recurrence after successful EET, but limited data exist on long-term (>5 year) recurrence outcomes. Our aim was to assess the incidence and predictors of long-term recurrence in a multicenter cohort of patients with T1 EAC treated with EET. METHODS: Patients with T1 EAC achieving successful endoscopic cancer eradication with a minimum of 5 years' clinical follow-up were included. The primary outcome was neoplastic recurrence, defined as dysplasia or EAC, and it was characterized as early (<2 years), intermediate (2-5 years), or late (>5 years). Predictors of recurrence were assessed by time to event analysis. RESULTS: A total of 84 T1 EAC patients (75 T1a, 9 T1b) with a median 9.1 years (range, 5.1-18.3 years) of follow-up were included. The overall incidence of neoplastic recurrence was 2.0 per 100 person-years of follow-up. Seven recurrences (3 dysplasia, 4 EAC) occurred after 5 years of EAC remission. Overall, 88% of recurrences were treated successfully endoscopically. EAC recurrence-related mortality occurred in 3 patients at a median of 5.2 years from EAC remission. Complete eradication of intestinal metaplasia was independently associated with reduced recurrence (hazard ratio, .13). CONCLUSIONS: Following successful EET of T1 EAC, neoplastic recurrence occurred after 5 years in 8.3% of cases. Careful long-term surveillance should be continued in this patient population. Complete eradication of intestinal metaplasia should be the therapeutic end point for EET.

Artificial intelligence-enhanced volumetric laser endomicroscopy improves dysplasia detection in Barrett's esophagus in a randomized cross-over study.

Volumetric laser endomicroscopy (VLE) is an advanced endoscopic imaging tool that can improve dysplasia detection in Barrett's esophagus (BE). However, VLE scans generate 1200 cross-sectional images that can make interpretation difficult. The impact of a new VLE artificial intelligence algorithm called Intelligent Real-time Image Segmentation (IRIS) is not well-characterized. This is a randomized prospective cross-over study of BE patients undergoing endoscopy who were randomized to IRIS-enhanced or unenhanced VLE first followed by the other (IRIS-VLE vs. VLE-IRIS, respectively) at expert BE centers. The primary outcome was image interpretation time, which served as a surrogate measure for ease of interpretation. The secondary outcome was diagnostic yield of dysplasia for each imaging modality. 133 patients were enrolled. 67 patients were randomized to VLE-IRIS and 66 to IRIS-VLE. Total interpretation time did not differ significantly between groups (7.8 min VLE-IRIS vs. 7 min IRIS-VLE, P = 0.1), however unenhanced VLE interpretation time was significantly shorter in the IRIS-VLE group (2.4 min vs. 3.8 min, P < 0.01). When IRIS was used first, 100% of dysplastic areas were identified, compared with 76.9% when VLE was the first interpretation modality (P = 0.06). IRIS-enhanced VLE reduced the time of subsequent unenhanced VLE interpretation, suggesting heightened efficiency and improved dysplasia detection. It was also able to identify all endoscopically non-visible dysplastic areas.

Acute Esophageal Necrosis: A Retrospective Cohort Study Highlighting the Mayo Clinic Experience.

OBJECTIVE: To describe the clinical, endoscopic, and histologic features in patients with acute esophageal necrosis (AEN). PATIENTS AND METHODS: In this retrospective cohort study, patients who were diagnosed as having AEN at Mayo Clinic sites in Minnesota, Florida, and Arizona between January 1, 1996, and January 31, 2021, were included. Data were collected on patient clinical characteristics and endoscopic and pathologic findings. RESULTS: The study included 79 patients with AEN with a median (range) age of 64 years (12 to 91 years); 53 (67.1%) were men. Predominant presenting symptoms were hematemesis (49 of 79 [62.0%]), abdominal pain (29 [36.7%]), and melena (20 [25.3%]). Shock was the triggering event for AEN in 49 (62.0%). The 30- and 90-day mortality were 24.0% (19 of 79) and 31.6% (25), respectively. The presence of coexisting infection or bacteremia was significantly associated with 90-day mortality (P<.01). Endoscopically, involvement of the distal third only, distal two-thirds only, and entire esophagus was observed in 31.6% (24 of 76), 39.5% (30), and 29.0% (22), respectively. The length of esophageal involvement correlated with duration of hospitalization (P=.05). The endoscopic appearance of the esophageal mucosa ranged from predominantly white (21 of 44 [47.7%]) to mixed white and black (13 [29.6%]) to predominantly black (10 [22.7%]), and sloughing was present in 18 (40.9%). In the 26 patients with histopathologic findings available for review, 25 (96.1%) had necrosis and/or ulceration with abundant pigmentation. Among the 79 patients, 39 (49.4%) had a follow-up esophagogastroduodenoscopy; 26 of these 39 patients (66.7%) had resolution while 5 had persistent AEN, 4 of whom had improvement. Esophageal strictures developed in 7 of the 39 patients (18.0%). CONCLUSION: Acute esophageal necrosis is a serious condition observed in critically ill patients. Its endoscopic appearance can be highly variable. In patients with an unclear diagnosis, esophageal biopsies may be helpful given the characteristic histologic findings.

Development of a deep learning model for the histologic diagnosis of dysplasia in Barrett's esophagus.

BACKGROUND AND AIMS: The risk of progression in Barrett's esophagus (BE) increases with development of dysplasia. There is a critical need to improve the diagnosis of BE dysplasia, given substantial interobserver disagreement among expert pathologists and overdiagnosis of dysplasia by community pathologists. We developed a deep learning model to predict dysplasia grade on whole-slide imaging. METHODS: We digitized nondysplastic BE (NDBE), low-grade dysplasia (LGD), and high-grade dysplasia (HGD) histology slides. Two expert pathologists confirmed all histology and digitally annotated areas of dysplasia. Training, validation, and test sets were created (by a random 70/20/10 split). We used an ensemble approach combining a "you only look once" model to identify regions of interest and histology class (NDBE, LGD, or HGD) followed by a ResNet101 model pretrained on ImageNet applied to the regions of interest. Diagnostic performance was determined for the whole slide. RESULTS: We included slides from 542 patients (164 NDBE, 226 LGD, and 152 HGD) yielding 8596 bounding boxes in the training set, 1946 bounding boxes in the validation set, and 840 boxes in the test set. When the ensemble model was used, sensitivity and specificity for LGD was 81.3% and 100%, respectively, and >90% for NDBE and HGD. The overall positive predictive value and sensitivity metric (calculated as F1 score) was .91 for NDBE, .90 for LGD, and 1.0 for HGD. CONCLUSIONS: We successfully trained and validated a deep learning model to accurately identify dysplasia on whole-slide images. This model can potentially help improve the histologic diagnosis of BE dysplasia and the appropriate application of endoscopic therapy.

Diagnostic performance of volumetric laser endomicroscopy for Barrett's esophagus dysplasia amongst gastroenterology trainees.

BACKGROUND: Volumetric laser endomicroscopy (VLE) is an advanced imaging modality used in Barrett's esophagus (BE) surveillance. VLE image interpretation is challenged by subtle grayscale image variation across a large amount of data. Training in VLE interpretation is not standardized. This study aims to determine if VLE training can be incorporated into a gastroenterology (GI) fellowship curriculum with the use of a self-directed module. METHODS: A standardized, self-directed training module (30 min) was created explaining the background and established VLE criteria for the diagnosis of BE dysplasia. A VLE image dataset was generated from a multicenter VLE database of targeted biopsies. GI trainees were asked to grade each image for the presence or absence of the following criteria (I) increased surface optical frequency domain imaging (OFDI) signal intensity and (II) atypical glands and provide a final diagnosis (dysplastic vs. non-dysplastic). Diagnostic performance was calculated and results compared to VLE expert interpretation using histology as the gold-standard. RESULTS: The dataset included 50 VLE images (10 high-grade dysplasia, 40 non-dysplastic BE). VLE images were reviewed in a randomized and blinded fashion by 5 GI trainees with no prior VLE experience and 5 experienced VLE users. Sensitivity, specificity and accuracy of GI trainees was 83.3% (95% CI: 71.5-91.7%), 59.0% (95% CI: 51.6-66.0%), and 64.8% (95% CI: 58.5-70.7%) compared to 80.0% (95% CI: 67.7-89.2%), 79.5% (95% CI: 73.0-85.0%), and 79.6% (95% CI: 74.1-84.4%) for VLE experts respectively. The difference in specificity and accuracy between the two groups were statistically significant with P<0.001. CONCLUSIONS: A brief training session on VLE is inadequate to reach competency in interpretation of VLE by GI trainees. Additional experience is required to accurately interpret VLE images.

Optimized Surveillance Intervals Following Endoscopic Eradication of Dysplastic Barrett's Esophagus: An International Cohort Study.

BACKGROUND & AIMS: Recommended surveillance intervals after complete eradication of intestinal metaplasia (CE-IM) after endoscopic eradication therapy (EET) are largely not evidence-based. Using recurrence rates in a multicenter international Barrett's esophagus (BE) CE-IM cohort, we aimed to generate optimal intervals for surveillance. METHODS: Patients with dysplastic BE undergoing EET and achieving CE-IM from prospectively maintained databases at 5 tertiary-care centers in the United States and the United Kingdom were included. The cumulative incidence of recurrence was estimated, accounting for the unknown date of actual recurrence that lies between the dates of current and previous endoscopy. This cumulative incidence of recurrence subsequently was used to estimate the proportion of patients with undetected recurrence for various surveillance intervals over 5 years. Intervals were selected that minimized recurrences remaining undetected for more than 6 months. Actual patterns of post-CE-IM follow-up evaluation are described. RESULTS: A total of 498 patients (with baseline low-grade dysplasia, 115 patients; high-grade dysplasia [HGD], 288 patients; and intramucosal adenocarcinoma [IMCa], 95 patients) were included. Any recurrence occurred in 27.1% and dysplastic recurrence occurred in 8.4% over a median of 2.6 years of follow-up evaluation. For pre-ablation HGD/IMCa, intervals of 6, 12, 18, and 24 months, and then annually, resulted in no patients with dysplastic recurrence undetected for more than 6 months, comparable with current guideline recommendations despite a 33% reduction in the number of surveillance endoscopies. For pre-ablation low-grade dysplasia, intervals of 1, 2, and 4 years balanced endoscopic burden and undetected recurrence risk. CONCLUSIONS: Lengthening post-CE-IM surveillance intervals would reduce the endoscopic burden after CE-IM with comparable rates of recurrent HGD/IMCa. Future guidelines should consider reduced surveillance frequency.

Prediction of Progression in Barrett's Esophagus Using a Tissue Systems Pathology Test: A Pooled Analysis of International Multicenter Studies.

BACKGROUND & AIMS: Prediction of progression risk in Barrett's esophagus (BE) may enable personalized management. We aimed to assess the adjunct value of a tissue systems pathology test (TissueCypher) performed on paraffin-embedded biopsy tissue, when added to expert pathology review in predicting incident progression, pooling individual patient-level data from multiple international studies METHODS: Demographics, clinical features, the TissueCypher risk class/score, and progression status were analyzed. Conditional logistical regression analysis was used to develop multivariable models predicting incident progression with and without the TissueCypher risk class (low, intermediate, high). Concordance (c-) statistics were calculated and compared with likelihood ratio tests to assess predictive ability of models. A risk prediction calculator integrating clinical variables and TissueCypher risk class was also developed. RESULTS: Data from 552 patients with baseline no (n = 472), indefinite (n = 32), or low-grade dysplasia (n = 48) (comprising 152 incident progressors and 400 non-progressors) were analyzed. A high-risk test class independently predicted increased risk of progression to high-grade dysplasia/adenocarcinoma (odds ratio, 6.0; 95% confidence interval, 2.9-12.0), along with expert confirmed low-grade dysplasia (odds ratio, 2.9; 95% confidence interval, 1.2-7.2). Model prediction of progression with the TissueCypher risk class incorporated was significantly superior than without, in the whole cohort (c-statistic 0.75 vs 0.68; P < .0001) and the nondysplastic BE subset (c-statistic 0.72 vs 0.63; P < .0001). Sensitivity and specificity of the high risk TissueCypher class were 38% and 94%, respectively. CONCLUSIONS: An objective tissue systems pathology test high-risk class is a strong independent predictor of incident progression in patients with BE, substantially improving progression risk prediction over clinical variables alone. Although test specificity was high, sensitivity was modest.