Introduction: Pancreatic exocrine insufficiency (PEI) results from a reduction in pancreatic secretion of enzymes, leading to malabsorption of nutrients, intestinal symptoms, nutritional deficiencies and related comorbidities. The diagnosis of pancreatic exocrine insufficiency should be based on digestive tests, mainly the coefficient of fat absorption (CFA), based on the quantification of 72 h fecal fat excretion (FFE). However, this test is rarely performed in clinical practice. Fecal elastase‐1 (FE‐1) is a simple and widely used alternative. This meta‐analysis evaluates the diagnostic accuracy of fecal elastase‐1 for the diagnosis of PEI diagnosed by CFA or 72h‐FFE.Methods: A systematic search of databases was performed to identify studies evaluating fecal elastase‐1 and CFA/FFE for the diagnosis of pancreatic exocrine insufficiency. Inclusion criteria required original studies with data on sensitivity, specificity and other diagnostic metrics. Two independent reviewers performed data extraction and quality assessment using the QUADAS‐2 tool. Pooled sensitivity, specificity, likelihood ratios and diagnostic odds ratio (DOR) were calculated and heterogeneity was assessed using I‐squared tests.Results: Thirteen studies with 888 patients were included. Fecal elastase‐1 at a cut‐off of 200 μg/g showed a pooled sensitivity and specificity of 0.94 and 0.69, respectively, with a DOR of 35.27. Lowering the cut‐off to 100 μg/g improved specificity to 0.82 but decreased sensitivity to 0.88. Subgroup analyses showed different diagnostic performance in different clinical contexts, with higher sensitivity in cystic fibrosis (0.98) and higher specificity in chronic pancreatitis (0.81). The positive and negative predictive values are limited in situations with low and high probability of pancreatic exocrine insufficiency, respectively.Conclusions: Fecal elastase‐1 is a sensitive and moderately specific diagnostic tool for pancreatic exocrine insufficiency and is suitable for initial screening in high‐risk populations. However, its moderate specificity requires careful interpretation in lower risk settings.

Introduction: Early stages of Crohn’s disease [CD] are predominantly inflammatory and early treatment could be useful to change the natural history of CD. We aimed to evaluate the impact of early treatment in our cohort of CD patients. Methods: We retrospectively reviewed clinical records of all CD patients at our centre who have received immunomodulators. Time from diagnosis to first CD-related major abdominal surgery or end of follow-up was considered. Dates of diagnosis, of starting immunomodulators (thiopurines / anti-tumour necrosis factor [TNF]), and of the first CD-related surgery when appropriate were collected. Results: Of 422 patients who received thiopurines, 189 operated patients started thiopurines after a median of 117 months (interquartile range [IQR] 44–196) since diagnosis; non-operated patients, after a median of 30 months [IQR 6–128], p < 0,005. Odds ratio [OR] for surgery was 1.006 (95% confidence interval [CI]1.004–1008) for each month of delay in starting thiopurines. Among 272 patients who received anti-TNFs, 137 operated patients started anti-TNFs after a median of 166 months [IQR 90–233] since diagnosis; non-operated patients after a median of 59 months [IQR 14–162]; p < 0,005. OR for surgery was 1.008 [95% CI 1.005–1.010] for each month of delay in starting anti-TNFs. Among 467 patients who received thiopurines and/or anti-TNF, 210 operated patients started any immunomodulator after a median of 120 months [IQR 48–197] since diagnosis and non-operated patients after a median of 30 months [IQR 6–126], p < 0,005. OR for surgery was 1.008 [95% CI 1.005–1.010] for each month of delay in starting immunomodulators. Conclusions: In our experience, time between diagnosis and thiopurine or anti-TNF initiation was associated with the risk of major abdominal surgery in Crohn’s disease.

Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures.

INTRODUCTION: Endoscopic ultrasound (EUS) allows for characterization and biopsy of pancreatic lesions. Pancreatic cystic neoplasms (PCN) include mucinous (M-PCN) and nonmucinous lesions (NM-PCN). Pancreatic ductal adenocarcinoma (P-DAC) is the commonest pancreatic solid lesion (PSL), followed by pancreatic neuroendocrine tumor (P-NET). Although 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). METHODS: A CNN was developed with 378 EUS examinations from 4 international reference centers (Centro Hospitalar Universitário São João, Hospital Universitario Puerta de Hierro Majadahonda, New York University Hospitals, Hospital das Clínicas Faculdade de Medicina da Universidade de São Paulo). About 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 data set was divided into a training and testing data set (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. RESULTS: 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. DISCUSSION: Our group developed the first worldwide CNN capable of detecting and differentiating the commonest PCN and PSL in EUS images, using examinations from 4 centers in 2 continents, minimizing the impact of the demographic bias. Larger multicentric studies are needed for technology implementation.

Background: Several artificial intelligence systems based on large language models (LLMs) have been commercially developed, with recent interest in integrating them for clinical questions. Recent versions now include image analysis capacity, but their performance in gastroenterology remains untested. This study assesses ChatGPT-4’s performance in interpreting gastroenterology images. Methods: A total of 740 images from five procedures—capsule endoscopy (CE), device-assisted enteroscopy (DAE), endoscopic ultrasound (EUS), digital single-operator cholangioscopy (DSOC), and high-resolution anoscopy (HRA)—were included and analyzed by ChatGPT-4 using a predefined prompt for each. ChatGPT-4 predictions were compared to gold standard diagnoses. Statistical analyses included accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). Results: For CE, ChatGPT-4 demonstrated accuracies ranging from 50.0% to 90.0%, with AUCs of 0.50–0.90. For DAE, the model demonstrated an accuracy of 67.0% (AUC 0.670). For EUS, the system showed AUCs of 0.488 and 0.550 for the differentiation between pancreatic cystic and solid lesions, respectively. The LLM differentiated benign from malignant biliary strictures with an AUC of 0.550. For HRA, ChatGPT-4 showed an overall accuracy between 47.5% and 67.5%. Conclusions: ChatGPT-4 demonstrated suboptimal diagnostic accuracies for image interpretation across several gastroenterology techniques, highlighting the need for continuous improvement before clinical adoption.

Background/Objectives: Endoscopic ultrasound (EUS) is crucial for lymph node (LN) characterization, playing a key role in oncological staging and treatment guidance. EUS criteria for predicting malignancy are imprecise, and histologic diagnosis may have limitations. This multicenter study aimed to evaluate the effectiveness of a novel artificial intelligence (AI)–based system in predicting LN malignancy from EUS images. Methods: This multicenter study included EUS images from nine centers. Lesions were labeled (“malignant” or “benign”) and delimited with bounding boxes. Definitive diagnoses were based on cytology/biopsy or surgical specimens and, if negative, a minimum six-month clinical follow-up. A convolutional neural network (CNN) was developed using the YOLO (You Only Look Once) architecture, incorporating both detection and classification modules. Results: A total of 59,992 images from 82 EUS procedures were analyzed. The CNN distinguished malignant from benign lymph nodes with a sensitivity of 98.8% (95% CI: 98.5–99.2%), specificity of 99.0% (95% CI: 98.3–99.7%), and precision of 99.0% (95% CI: 98.4–99.7%). The negative and positive predictive values for malignancy were 98.8% and 99.0%, respectively. Overall diagnostic accuracy was 98.3% (95% CI: 97.6–99.1%). Conclusions: This is the first study evaluating the performance of deep learning systems for LN assessment using EUS imaging. Our AI-powered imaging model shows excellent detection and classification capabilities, emphasizing its potential to provide a valuable tool to refine LN evaluation with EUS, ultimately supporting more tailored, efficient patient care.