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Background: Capsule endoscopy (CE) is a valuable tool for assessing inflammation in patients with Crohn's disease (CD). The current standard for evaluating inflammation are validated scores (and clinical laboratory values) like Lewis score (LS), Capsule Endoscopy Crohn's Disease Activity Index (CECDAI), and ELIAKIM. Recent advances in artificial intelligence (AI) have made it possible to automatically select the most relevant frames in CE. Objectives: In this proof-of-concept study, our objective was to develop an automated scoring system using CE images to objectively grade inflammation. Design: Pan-enteric CE videos (PillCam Crohn's) performed in CD patients between 09/2020 and 01/2023 were retrospectively reviewed and LS, CECDAI, and ELIAKIM scores were calculated. Methods: We developed a convolutional neural network-based automated score consisting of the percentage of positive frames selected by the algorithm (for small bowel and colon separately). We correlated clinical data and the validated scores with the artificial intelligence-generated score (AIS). Results: A total of 61 patients were included. The median LS was 225 (0–6006), CECDAI was 6 (0–33), ELIAKIM was 4 (0–38), and SB_AIS was 0.5659 (0–29.45). We found a strong correlation between SB_AIS and LS, CECDAI, and ELIAKIM scores (Spearman's r = 0.751, r = 0.707, r = 0.655, p = 0.001). We found a strong correlation between LS and ELIAKIM (r = 0.768, p = 0.001) and a very strong correlation between CECDAI and LS (r = 0.854, p = 0.001) and CECDAI and ELIAKIM scores (r = 0.827, p = 0.001). Conclusion: Our study showed that the AI-generated score had a strong correlation with validated scores indicating that it could serve as an objective and efficient method for evaluating inflammation in CD patients. As a preliminary study, our findings provide a promising basis for future refining of a CE score that may accurately correlate with prognostic factors and aid in the management and treatment of CD patients. Plain language summary: Artificial intelligence in Crohn's disease: the development of an automated score for disease activity evaluation This study introduces an innovative AI-based approach to evaluate Crohn's Disease. The AI system automatically analyzes images from capsule endoscopy, focusing on finding ulcers and erosions to measure disease activity. The research reveals a robust correlation between the AI-generated score assessing inflammation in the small bowel and traditional clinical scores. This suggests that the AI solution could be a quicker and more consistent way to evaluate Crohn's Disease, speeding up the evaluation process and reducing manual scoring variability. While promising, the study acknowledges limitations and emphasizes the need for further validation with larger groups of patients. Overall, it represents a crucial step toward integrating AI into gastroenterology, offering a glimpse into a future of more objective and personalized Crohn's Disease evaluation. [ABSTRACT FROM AUTHOR]

Background Inflammatory bowel disease (IBD) may be associated with a wide range of hepatobiliary manifestations. This study aimed to characterize the spectrum of hepatobiliary disorders in patients with IBD who underwent liver biopsy for sustained abnormal liver function tests (LFT). Method A retrospective study was performed of all patients with IBD who underwent liver biopsy between January 2010 and December 2020 for sustained abnormal LFT (at least 6-month duration). Results A total of 101 patients were included, mostly male (62.4%), with a mean age of 44.4±13.3 years. The most common IBD type was Crohn's disease (61.4%). Median time interval between abnormal LFT and biopsy was 14 (7-36) months. Abnormal LFT was predominantly hepatocellular in 40 patients (39.6%), cholestatic in 26 (25.7%) and mixed in 35 (34.7%). The most frequent diseases were nonalcoholic fatty liver disease (NAFLD) in 33 patients (32.7%), drug-induced liver disease (DILI) in 30 (29.7%), autoimmune hepatitis (AIH) in 13 (12.9%) and primary sclerosing cholangitis (PSC) in 13 (12.9%). Three patients had primary biliary cholangitis. Remarkably, 70 patients (69.3%) already had fibrosis by the time of liver biopsy and in 6 (5.9%) liver disease was already detected in the stage of cirrhosis. Conclusions Abnormal LFT in IBD patients had a wide range of etiologies and histology was often essential for reaching a correct diagnosis. NAFLD, DILI, AIH and PSC were the most common diagnoses and patients often presented in cirrhotic stage. Therefore, liver biopsy must be considered early in IBD patients with unexplained sustained abnormal LFT. [ABSTRACT FROM AUTHOR]

Background and Aim: Colon capsule endoscopy (CCE) has become a minimally invasive alternative for conventional colonoscopy. Nevertheless, each CCE exam produces between 50 000 and 100 000 frames, making its analysis time‐consuming and prone to errors. Convolutional neural networks (CNNs) are a type of artificial intelligence (AI) architecture with high performance in image analysis. This study aims to develop a CNN model for the identification of colonic ulcers and erosions in CCE images. Methods: A CNN model was designed using a database of CCE images. A total of 124 CCE exams performed between 2010 and 2020 in two centers were reviewed. For CNN development, a total of 37 319 images were extracted, 33 749 showing normal colonic mucosa and 3570 showing colonic ulcers and erosions. Datasets for CNN training, validation, and testing were created. The performance of the algorithm was evaluated regarding its sensitivity, specificity, positive and negative predictive values, accuracy, and area under the curve. Results: The network had a sensitivity of 96.9% and a specificity of 99.9% specific for the detection of colonic ulcers and erosions. The algorithm had an overall accuracy of 99.6%. The area under the curve was 1.00. The CNN had an image processing capacity of 90 frames per second. Conclusions: The developed algorithm is the first CNN‐based model to accurately detect ulcers and erosions in CCE images, also providing a good image processing performance. The development of these AI systems may contribute to improve both the diagnostic and time efficiency of CCE exams, facilitating CCE adoption to routine clinical practice. [ABSTRACT FROM AUTHOR]