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Endoscopic ultrasound (EUS) allows characterization and biopsy of pancreatic lesions. Pancreatic cystic neoplasms (PCN) include in mucinous (M-PCN) and non-mucinous lesions (NM-PCN). Pancreatic ductal adenocarcinoma (P-DAC) is the commonest pancreatic solid lesion (PSL), followed by pancreatic neuroendocrine tumor (P-NET). While EUS is preferred for pancreatic lesion evaluation, its diagnostic accuracy is suboptimal. This multicentric study aims to develop a convolutional neural network (CNN) for detecting and distinguishing PCN (namely M-PCN and NM-PCN) and PSL (particularly P-DAC and P-NET). A CNN was developed with 378 EUS exams from 4 international reference centers (Centro Hospitalar Universitário São João, Hospital Universitario Puerta de Hierro Majadahonda, New York University Hospitals, Hospital das Clínicas FMUSP). 126.000 images were obtained - 19.528 M-PCN, 8.175 NM-PCN, 64.286 P-DAC, 29.153 P-NET and 4.858 normal pancreas images. A trinary CNN differentiated normal pancreas tissue from M-PCN and NM-PCN. A binary CNN distinguished P-DAC from P-NET. The total dataset was divided in a training and testing dataset (used for model's evaluation) in a 90/10% ratio. The model was evaluated through its sensitivity, specificity, positive and negative predictive values and accuracy. The CNN had 99.1% accuracy for identifying normal pancreatic tissue, 99.0% and 99.8% for M-PCN and NM-PCN, respectively. P-DAC and P-NET were distinguished with 94.0% accuracy. Our group developed the first worldwide CNN capable of detecting and differentiating the commonest PCN and PSL in EUS images, using exams from 4 centers in two continents, minimizing the impact of the demographic bias. Larger multicentric studies are needed for technology implementation., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Introduction: High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell carcinoma (ASCC) precursors. Preliminary studies on the application of artificial intelligence (AI) models to this modality have revealed promising results. However, the impact of staining techniques and anal manipulation on the effectiveness of these algorithms has not been evaluated. We aimed to develop a deep learning system for automatic differentiation of high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion in HRA images in different subsets of patients (nonstained, acetic acid, lugol, and after manipulation)., Methods: A convolutional neural network was developed to detect and differentiate high-grade and low-grade anal squamous intraepithelial lesions based on 27,770 images from 103 HRA examinations performed in 88 patients. Subanalyses were performed to evaluate the algorithm's performance in subsets of images without staining, acetic acid, lugol, and after manipulation of the anal canal. The sensitivity, specificity, accuracy, positive and negative predictive values, and area under the curve were calculated., Results: The convolutional neural network achieved an overall accuracy of 98.3%. The algorithm had a sensitivity and specificity of 97.4% and 99.2%, respectively. The accuracy of the algorithm for differentiating high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion varied between 91.5% (postmanipulation) and 100% (lugol) for the categories at subanalysis. The area under the curve ranged between 0.95 and 1.00., Discussion: The introduction of AI to HRA may provide an accurate detection and differentiation of ASCC precursors. Our algorithm showed excellent performance at different staining settings. This is extremely important because real-time AI models during HRA examinations can help guide local treatment or detect relapsing disease., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Introduction: Capsule endoscopy (CE) is a minimally invasive examination for evaluating the gastrointestinal tract. However, its diagnostic yield for detecting gastric lesions is suboptimal. Convolutional neural networks (CNNs) are artificial intelligence models with great performance for image analysis. Nonetheless, their role in gastric evaluation by wireless CE (WCE) has not been explored., Methods: Our group developed a CNN-based algorithm for the automatic classification of pleomorphic gastric lesions, including vascular lesions (angiectasia, varices, and red spots), protruding lesions, ulcers, and erosions. A total of 12,918 gastric images from 3 different CE devices (PillCam Crohn's; PillCam SB3; OMOM HD CE system) were used from the construction of the CNN: 1,407 from protruding lesions; 994 from ulcers and erosions; 822 from vascular lesions; and 2,851 from hematic residues and the remaining images from normal mucosa. The images were divided into a training (split for three-fold cross-validation) and validation data set. The model's output was compared with a consensus classification by 2 WCE-experienced gastroenterologists. The network's performance was evaluated by its sensitivity, specificity, accuracy, positive predictive value and negative predictive value, and area under the precision-recall curve., Results: The trained CNN had a 97.4% sensitivity; 95.9% specificity; and positive predictive value and negative predictive value of 95.0% and 97.8%, respectively, for gastric lesions, with 96.6% overall accuracy. The CNN had an image processing time of 115 images per second., Discussion: Our group developed, for the first time, a CNN capable of automatically detecting pleomorphic gastric lesions in both small bowel and colon CE devices., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Introduction: Anorectal manometry (ARM) is the gold standard for the evaluation of anorectal functional disorders, prevalent in the population. Nevertheless, the accessibility to this examination is limited, and the complexity of data analysis and report is a significant drawback. This pilot study aimed to develop and validate an artificial intelligence model to automatically differentiate motility patterns of fecal incontinence (FI) from obstructed defecation (OD) using ARM data., Methods: We developed and tested multiple machine learning algorithms for the automatic interpretation of ARM data. Four models were tested: k-nearest neighbors, support vector machines, random forests, and gradient boosting (xGB). These models were trained using a stratified 5-fold strategy. Their performance was assessed after fine-tuning of each model's hyperparameters, using 90% of data for training and 10% of data for testing., Results: A total of 827 ARM examinations were used in this study. After fine-tuning, the xGB model presented an overall accuracy (84.6% ± 2.9%), similar to that of random forests (82.7% ± 4.8%) and support vector machines (81.0% ± 8.0%) and higher that of k-nearest neighbors (74.4% ± 3.8%). The xGB models showed the highest discriminating performance between OD and FI, with an area under the curve of 0.939., Discussion: The tested machine learning algorithms, particularly the xGB model, accurately differentiated between FI and OD manometric patterns. Subsequent development of these tools may optimize the access to ARM studies, which may have a significant impact on the management of patients with anorectal functional diseases., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Introduction: Device-assisted enteroscopy (DAE) plays a major role in the investigation and endoscopic treatment of small bowel diseases. Recently, the implementation of artificial intelligence (AI) algorithms to gastroenterology has been the focus of great interest. Our aim was to develop an AI model for the automatic detection of protruding lesions in DAE images., Methods: A deep learning algorithm based on a convolutional neural network was designed. Each frame was evaluated for the presence of enteric protruding lesions. The area under the curve, sensitivity, specificity, and positive and negative predictive values were used to assess the performance of the convolutional neural network., Results: A total of 7,925 images from 72 patients were included. Our model had a sensitivity and specificity of 97.0% and 97.4%, respectively. The area under the curve was 1.00., Discussion: Our model was able to efficiently detect enteric protruding lesions. The development of AI tools may enhance the diagnostic capacity of deep enteroscopy techniques., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Introduction: Characterization of biliary strictures is challenging. Papillary projections (PP) are often reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy. In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of PP in digital single-operator cholangioscopy images., Methods: A convolutional neural network (CNN) was developed. Each frame was evaluated for the presence of PP. The CNN's performance was measured by the area under the curve, sensitivity, specificity, and positive and negative predictive values., Results: A total of 3,920 images from 85 patients were included. Our model had a sensitivity and specificity 99.7% and 97.1%, respectively. The area under the curve was 1.00., Discussion: Our CNN was able to detect PP with high accuracy. Future development of AI tools may optimize the macroscopic characterization of biliary strictures., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Introduction: Dipeptidyl peptidase-4 (DPP-4) is a membrane-bound glycoprotein that acts as a receptor but also exists in a soluble form. It has been recognized as a mediator of inflammation and considered a biomarker in inflammatory bowel disease (IBD)., Methods: We evaluated a prospectively recruited cohort, consisting of 101 patients with IBD, using validated clinical indexes; 22 patients with ulcerative colitis (UC) underwent endoscopic evaluation. Fecal DPP-4 (fDPP-4) levels were analyzed and correlated with clinical scores, Mayo endoscopic score (in UC patients), serum DPP-4, C-reactive protein, and fecal calprotectin. Immunohistochemical staining for DPP-4 in intestinal biopsies was also performed., Results: When compared with remitters, median fDPP-4 levels were higher in patients with ileal Crohn's disease (CD) (7,584 [1,464-7,816] vs 2,104 [630-2,676] ng/mL, P = 0.015) and lower in patients with UC exhibiting clinical activity (1,213 [559-1,682] vs 7,814 [2,555-7,985] ng/mL, P < 0.001). Patients with UC presenting endoscopic activity also had lower levels than remitters (939 [559-1,420] vs 7,544 [4,531-7,940] ng/mL, P = 0.006). Fecal DPP-4 discriminated clinical activity from remission with areas under the curve of 0.76 (95% confidence interval [CI] 0.58-0.94, P = 0.015) and 0.80 (95% CI 0.68-0.93, P < 0.001) in CD and UC, respectively; it allowed to differentiate endoscopic activity in patients with UC, with areas under the curve of 0.84 (95% CI 0.63-1.00, P = 0.009). Immunohistochemical analysis revealed higher DPP-4 apical expression in UC remitters, but no statistically significant differences were revealed between patients with ileal CD., Discussion: Our results suggest that fDPP-4 can be used as a biomarker of IBD activity, particularly in UC. The expression profiles in intestinal tissue might represent a functional compartmentalization of DPP-4 expression., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Combined hepatocellular-cholangiocarcinoma (cHCC-CC) is a unique entity that contains mixed elements of both hepatocellular carcinoma and cholangiocarcinoma. We report a 62-year-old woman with alcoholic cirrhosis with elevated α-fetoprotein of 25.3 ng/mL. Abdominal computed tomography showed a poorly defined subcapsular nodular lesion in the VIII segment, showing enhancement during the arterial phase and washout in the delayed phase. Histological examination of hepatic segmentectomy revealed a malignant epithelial neoplasia constituted by 2 distinct components, consistent with the diagnosis of cHCC-CC, classical type. One year after surgical resection, the patient noticed a nodule in the right breast. Histological examination of core needle biopsy was compatible with a metastasis in the breast of the previously diagnosed liver cancer. To our knowledge, this is the first report of breast metastases from a cHCC-CC, denoting disseminated metastatic disease and poor prognosis., (© 2019 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Objectives: Crohn's disease (CD) induces cumulative structural damage, initially characterized by a non-stenosing non-penetrating behavior (B1) with progression over time to a fibro-stenosing (B2) and/or penetrating phenotype (B3). Our aim was to assess the long-term evolution of disease behavior of CD and determine what factors predict phenotype progression., Methods: This was a study based on prospectively collected data from a CD database in an inflammatory bowel disease outpatient clinic. B1 corresponds to a non-stenosing non-penetrating disease, B2 to a stenosing behavior, and B3 to a penetrating one., Results: Seven hundred and thirty-six patients with CD (368 female) were followed up for 12.3 years (± 8.4), with 87.0% of them exhibiting B1 phenotype at diagnosis. Of these patients, 28.5% progressed to B2 phenotype and 23.5% to B3. Fifty percent of the patients started azathioprine treatment before phenotype change and 13.9% started anti-tumor necrosis factor-α (anti-TNFα) treatment before phenotype change. Monotherapy with azathioprine before phenotype change as well as combination therapy with azathioprine/anti-TNFα before phenotype change delayed disease progression (B1-B2 or B3) in comparison with patients who did not receive treatment (P<0.001). The hazard ratio (HR) for disease progression was lower for both monotherapy with azathioprine (HR: 0.15, P<0.001) or combination therapy with anti-TNFα (HR: 0.33, P<0.001). Upper gastrointestinal tract involvement, male gender, and steroid use were associated with an early progression of phenotype from B1 to B2 or B3 (P<0.001). The HR for disease progression was higher in patients who used steroids without criteria of dependence or resistance (HR: 2.67, P<0.001) and was even higher in patients with criteria of dependence or resistance (HR: 6.44, P<0.001). Longer delays between CD diagnosis and beginning of therapy with azathioprine and/or anti-TNFα were associated with disease progression. The longer the duration of treatment, the less likely the disease progression., Conclusions: Monotherapy with azathioprine before behavior change as well as combination therapy with azathioprine and anti-TNFα before behavior change delays phenotype progression of CD, whereas upper gastrointestinal tract involvement, male gender, and steroid use with or without criteria of steroid dependence are associated with a higher risk for disease progression.