Trending Topics in Gastroenterology | GastroAGI

AI for Endoscopic and Histologic Assessment in IBD Trials: J Crohn’s and Colitis, March 2026

Introduction Accurate assessment of disease activity in inflammatory bowel disease (IBD) clinical trials relies on central reading of endoscopic and histologic images. Although considered the current gold standard, this process is limited by interobserver variability, operational delays, high costs, and scalability challenges. With rapid advances in artificial intelligence (AI) and machine learning (ML), automated image analysis has emerged as a promising tool to improve the accuracy, consistency, and efficiency of endpoint assessment in clinical trials. To address this evolving field, the International Organisation for the Study of IBD (IOIBD) developed an evidence-based consensus on the appropriate use of AI-assisted evaluation of endoscopic and histologic endpoints in IBD trials. Summary This consensus initiative involved a literature review (2018–2025) on AI applications in IBD endoscopy and histopathology. Based on the available evidence, the steering committee formulated 36 statements, which were voted on by 72 IOIBD experts, with ≥80% agreement required for consensus. A total of 45 experts completed the survey, and consensus was achieved for 28 statements. The panel concluded that AI-assisted central reading could significantly enhance diagnostic accuracy, improve reproducibility, reduce costs, and accelerate image interpretation in IBD trials. Importantly, the consensus strongly favoured a hybrid model combining human expert review with AI support, rather than complete replacement of human interpretation. However, several challenges remain. These include limited external validation of AI models, concerns regarding generalizability across populations and endoscopy platforms, and dependence on high-quality human-annotated training datasets. Conclusion The IOIBD consensus supports the integration of AI/ML tools into endoscopic and histologic assessment in IBD clinical trials, emphasising AI-augmented human decision-making. Future efforts should focus on validation, regulatory guidance, and multimodal data integration, which may ultimately enable broader use of AI in both clinical research and routine IBD care.

AI-Enabled Imaging for Predicting Postoperative Recurrence in Crohn’s Disease: Gut, March 2026

Postoperative recurrence (POR) remains a major challenge in Crohn’s disease (CD), occurring in up to 70% of patients within the first year after intestinal resection. Current surveillance strategies—primarily ileocolonoscopy and fecal calprotectin—have limited accuracy and lack standardized monitoring algorithms. Emerging technologies are reshaping the approach to POR detection. Advanced endoscopic imaging techniques, including confocal laser endomicroscopy and endocytoscopy, allow real-time microscopic evaluation of the anastomosis and early mucosal changes. Intestinal ultrasound and cross-sectional imaging provide non-invasive transmural assessment, improving monitoring beyond mucosal disease. Simultaneously, multi-omics approaches (genomics, transcriptomics, proteomics, metabolomics, and microbiome profiling) are identifying biological pathways associated with recurrence risk. Integration of these datasets using artificial intelligence (AI) may enable predictive multimodal models that combine clinical, imaging, and molecular data. Such AI-driven strategies could allow personalized risk stratification, earlier detection of recurrence, and tailored postoperative therapy, moving Crohn’s disease management toward precision medicine. However, prospective validation and clinical workflow integration are still required.

ACG Delphi Consensus on AI in GI- AMJ Feb.26

This American College of Gastroenterology (ACG)–led modified Delphi consensus provides a comprehensive framework for responsible integration of artificial intelligence (AI) into gastroenterology, hepatology, and endoscopy practice. A multidisciplinary task force of 32 experts and 12 industry partners reviewed the literature across five domains: endoscopy, clinical/practice management applications, IBD and liver disease, training/education, and ethics/equity. Out of 43 proposed statements, 100% ultimately reached consensus (≥70% agreement). In endoscopy, strong evidence supports computer-aided detection (CADe) for improving adenoma detection rates and reducing miss rates in controlled settings, though real-world impact and long-term outcomes (e.g., interval cancer reduction) remain uncertain. The panel emphasizes robust validation using heterogeneous datasets to mitigate bias. Beyond endoscopy, promising applications include ambient AI scribes, natural language processing for coding, workflow optimization, and prior authorization support. In IBD and hepatology, AI shows potential in improving diagnostic accuracy, risk stratification, and therapeutic guidance. Importantly, the consensus stresses that AI should augment—not replace—clinical expertise. Recommendations include structured AI curricula for trainees while preserving independent procedural competence to prevent “deskilling.” Ethical priorities focus on data governance, chain-of-custody protections, bias mitigation, specialty oversight, and equitable reimbursement models. Future priorities include prospective pragmatic trials, multi-institutional data-sharing consortia, and transparent validation standards to ensure safe, effective, and equitable AI adoption in gastrointestinal practice.

Cholangioscopy in biliary tract disease(Endoscopy, Jan-2026)

Cholangioscopy is an advanced endoscopic procedure that allows direct visualization of the bile ducts and is commonly used to evaluate biliary tract diseases, including strictures, stones, and malignancies. It is particularly valuable in cases where traditional imaging modalities, such as ERCP, fail to provide definitive diagnoses. Cholangioscopy can facilitate targeted biopsies, therapeutic interventions (e.g., lithotripsy for bile duct stones), and aid in distinguishing benign from malignant biliary strictures. Recent advancements in artificial intelligence (AI) have significantly improved the diagnostic accuracy of cholangioscopy. For example, studies have shown that AI systems analyzing cholangioscopy footage outperform traditional sampling methods like brush cytology and forceps biopsy in identifying malignancy. AI systems, with sensitivity and specificity rates as high as 80.0% and 90.3%, respectively, can enhance clinical decision-making and reduce diagnostic uncertainty.

Cholangioscopy in biliary tract disease (Thieme, Jan-2026)

Cholangioscopy is a specialized endoscopic procedure that allows direct visualization of the bile ducts, aiding in the diagnosis and management of biliary tract diseases such as strictures, stones, and malignancies. It is particularly valuable when conventional imaging and sampling techniques, such as brush cytology or forceps biopsy during ERCP, fail to provide definitive results. Cholangioscopy facilitates targeted biopsies and enhances diagnostic accuracy. Recent advancements include the integration of artificial intelligence (AI) systems to analyze cholangioscopy footage. Studies, such as the one described in the provided context, demonstrate that AI systems can significantly outperform traditional ERCP sampling modalities in diagnosing malignancy. The AI system reviewed was shown to achieve higher sensitivity, specificity, and overall accuracy in classifying biliary strictures, making it a promising tool for improving diagnostic confidence and patient outcomes.

Laboratory-Based ML Model for Predicting Advanced Fibrosis in MASLD

The study described focuses on the development of a laboratory-based machine learning (ML) model aimed at predicting advanced liver fibrosis in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Below is a detailed explanation of the model and its significance: ### **Background** MASLD is a condition characterized by fat accumulation in the liver due to metabolic dysfunction, and it can silently progress to advanced liver fibrosis, which poses severe health risks. Traditionally, liver fibrosis is diagnosed using invasive liver biopsy, which is not only uncomfortable for patients but also costly and resource-intensive. Noninvasive methods like imaging and blood-based tests exist but have limitations in accuracy and accessibility. To address these challenges, the researchers developed a machine learning-based model that utilizes routine clinical and laboratory data to predict advanced fibrosis. ### **Study Design** The research was conducted as a retrospective analysis involving patients with biopsy-confirmed MASLD. The key components of the study included: - **Data Integration**: Combining demographic data, clinical history, and standard laboratory test results to create a comprehensive dataset. - **Algorithm Evaluation**: Testing multiple machine learning algorithms to identify the optimal model for distinguishing advanced fibrosis from non-advanced stages. - **Feature Selection**: Reducing the complexity of the model by identifying a minimal set of variables that maintain predictive accuracy. ### **Key Findings** - **Best Performing Algorithm**: The Extra Trees classifier emerged as the most effective machine learning model. It demonstrated robust predictive performance and consistency across various validation settings, making it suitable for clinical use. - **Simplified Model**: To improve practicality, the model was refined to rely on a small set of commonly available clinical and laboratory parameters. This simplification ensures easy integration into routine care without requiring specialized tools or expertise. - **Web-Based Tool**: The finalized model was deployed as an accessible, web-based application. This allows clinicians to assess the risk of advanced fibrosis in MASLD patients noninvasively and efficiently. ### **Clinical Significance** 1. **Noninvasive Assessment**: The ML model eliminates the need for invasive liver biopsy, reducing patient discomfort and healthcare costs. 2. **Accessibility**: By relying on routinely available laboratory data, the model can be used in resource-limited settings where specialized diagnostic equipment may not be available. 3. **Early Detection**: The ability to identify advanced fibrosis at earlier stages can improve patient outcomes by enabling timely intervention and management. 4. **Scalability**: The web-based deployment ensures that the tool can be widely adopted in clinical practice, supporting scalable fibrosis assessment. ### **Future Implications** With further external validation, this machine learning approach has the potential to transform the evaluation of liver fibrosis in MASLD patients. It could be integrated into routine workflows to assist in: - Risk stratification for MASLD patients. - Guiding clinical decision-making, such as prioritizing patients for more intensive monitoring or treatment. - Enhancing care in underserved areas where invasive diagnostic methods are impractical. ### **Conclusion** The laboratory-based ML model for predicting advanced fibrosis in MASLD represents a significant step forward in noninvasive, accessible, and scalable liver disease evaluation. By leveraging machine learning and commonly available clinical data, this tool has the potential to improve the detection and management of advanced fibrosis, ultimately enhancing patient outcomes and reducing healthcare burdens.

Trial of Artificial Intelligence and Adjunctive Polyp Detection - J of JGH - Jan,26

The study referenced, titled "Trial of Artificial Intelligence and Adjunctive Polyp Detection," published in the Journal of Gastroenterology and Hepatology (JGH) on January 26, evaluates the effectiveness of artificial intelligence (AI)-assisted polyp detection systems in improving adenoma detection rates during colonoscopy procedures. The trial focused on whether combining AI technology with established colonoscopy techniques could further enhance detection outcomes. ### Key Details of the Study: 1. **Objective**: The study aimed to determine the incremental value of AI-based polyp detection systems when used alongside traditional colonoscopy practices, such as extended withdrawal time, retroflexion, patient positioning adjustments, and advanced imaging techniques. 2. **Methodology**: - **Design**: A prospective randomized controlled trial conducted at a single hospital. - **Participants**: Multiple experienced endoscopists performed colonoscopies, with patients randomized into two groups: one using AI-assisted detection and the other following conventional procedures. - **Adjunctive Techniques**: Endoscopists were allowed to use supplementary detection-enhancing techniques based on clinical judgment rather than strict protocols. 3. **Findings**: - **AI's Impact**: Colonoscopies supported by AI demonstrated a trend toward improved adenoma detection rates compared to standard procedures. - **Screening Colonoscopy Benefits**: The use of AI was particularly beneficial in patients undergoing screening colonoscopies, leading to higher numbers of detected polyps and overall detection performance. - **Role of Conventional Practices**: Traditional practices such as extended withdrawal time and advanced imaging techniques were strongly associated with improved outcomes. AI remained an independent contributor to better adenoma detection rates when combined with these practices. 4. **Conclusion**: - AI-assisted polyp detection systems significantly enhance adenoma detection, even when used by experienced endoscopists. - The effectiveness of AI is maximized when integrated with established procedural strategies, emphasizing the importance of combining technological advancements with meticulous colonoscopy practices to achieve optimal detection performance. This study underscores the potential of AI in advancing colorectal cancer prevention by improving adenoma detection rates, particularly in screening settings. It advocates for the integration of AI technology with high-quality procedural techniques to optimize patient outcomes.

Perihilar Cholangiocarcinoma and Deep Learning

### Perihilar Cholangiocarcinoma (pCCA) and Deep Learning #### **What is Perihilar Cholangiocarcinoma (pCCA)?** Perihilar cholangiocarcinoma (pCCA) is a type of bile duct cancer that arises near the liver's hilum, where the bile ducts exit the liver. It is the most common type of cholangiocarcinoma (bile duct cancer) and is often associated with primary sclerosing cholangitis (PSC), a chronic liver disease characterized by inflammation and scarring of the bile ducts. Unfortunately, pCCA is frequently diagnosed at a late stage, making it one of the leading causes of mortality in PSC patients. Early detection of pCCA is critical because it allows for curative treatments such as liver transplantation or surgical resection. --- #### **What is Deep Learning?** Deep learning is a subset of artificial intelligence (AI) and machine learning that uses neural networks with multiple layers to analyze complex datasets. A deep learning model "learns" patterns and features from raw data, such as medical images, without requiring explicit programming. It is particularly useful in image analysis, where it can identify subtle, complex patterns that may be missed by human experts. In this study, a specific deep learning architecture called **3D DenseNet-121** was used. This is a convolutional neural network (CNN) designed to process three-dimensional (3D) medical imaging data, such as MRI scans. DenseNet-121 is known for its efficiency in feature extraction and its ability to learn hierarchical representations of the data, making it well-suited for detecting abnormalities in medical images. --- #### **How MRI Helps Identify the Severity of PSC** Magnetic Resonance Imaging (MRI), particularly contrast-enhanced MRI, is a non-invasive imaging technique that provides detailed visualization of the liver, bile ducts, and surrounding tissues. In patients with PSC, MRI can help identify: 1. **Bile Duct Abnormalities**: PSC causes strictures (narrowing) and dilation of bile ducts, which can be visualized on MRI. 2. **Mass Lesions**: The presence of a mass or thickening in the bile ducts may indicate cholangiocarcinoma. 3. **Liver Damage**: MRI can assess liver fibrosis, cirrhosis, or other complications of PSC. 4. **Vascular Involvement**: MRI can reveal whether cancer has invaded nearby blood vessels, which is critical for staging and treatment planning. However, early-stage pCCA is often challenging to detect using MRI alone because the cancer may not form a distinct mass or may blend with the background fibrosis and inflammation caused by PSC. This is where deep learning can play a transformative role. --- #### **How Deep Learning Analyzes MRI to Predict Cancer in PSC** Deep learning models, like the 3D DenseNet-121 used in this study, analyze MRI images to detect patterns indicative of early-stage pCCA. Here's how the process works: 1. **Data Input**: - The model is trained on a dataset of MRI images from patients with and without pCCA. - Images are labeled based on clinical diagnoses, such as "pCCA present" or "pCCA absent." 2. **Feature Extraction**: - The model automatically learns to identify features associated with pCCA, such as subtle changes in bile duct structure, tissue texture, or contrast enhancement patterns. - Unlike traditional methods, the model does not require manual feature engineering by radiologists. 3. **Prediction**: - Once trained, the model analyzes new MRI images and predicts whether pCCA is present or absent. - The model outputs sensitivity (ability to detect true positives) and specificity (ability to avoid false positives). 4. **Performance Comparison**: - In this study, the deep learning model significantly outperformed expert radiologists in detecting early-stage pCCA: - **Sensitivity**: The model detected 87.9% of pCCA cases compared to 50% by radiologists. - **Specificity**: The model had a specificity of 84.1%, slightly lower than the radiologists' 100%, but still clinically acceptable. - **Area Under the Receiver Operating Curve (AUC)**: The model achieved an AUC of 86.0%, compared to 75.0% for radiologists. 5. **Mass-Independent Detection**: - Even in cases where no visible mass was present (a common scenario in early-stage pCCA), the model achieved a higher sensitivity (91.6%) than radiologists (50.6%). --- #### **Advantages of Deep Learning in pCCA Detection** 1. **Early Detection**: The model can identify subtle changes in MRI that may indicate early-stage pCCA, even before a mass is visible. 2. **Improved Sensitivity**: Fewer cases of cancer are missed compared to radiologists. 3. **Efficiency**: The model can analyze large volumes of MRI data quickly and consistently. 4. **Non-Invasive**: Deep learning enhances the diagnostic power of existing imaging techniques without requiring additional invasive procedures. --- #### **Conclusion** The application of deep learning, specifically the 3D DenseNet-121 model, represents a significant advancement in the detection of perihilar cholangiocarcinoma in patients with primary sclerosing cholangitis. By leveraging MRI data, the model can detect early-stage pCCA with higher sensitivity and comparable specificity to expert radiologists. This technology has the potential to improve outcomes by enabling earlier diagnosis and timely curative interventions for PSC patients at risk of developing pCCA.

Multicenter Validation of an AI-Based Cholangioscopy System for Biliary Disease Evaluation

The multicenter validation study focused on assessing the performance of an artificial intelligence (AI)-based system for evaluating biliary tract disease using cholangioscopy video footage. Accurate differentiation between benign and malignant biliary strictures is a major clinical challenge, as traditional diagnostic methods, such as endoscopic retrograde cholangiopancreatography (ERCP)-based brush cytology and forceps biopsy, are limited by their low sensitivity. Cholangioscopy offers direct visualization and targeted sampling of biliary pathology, but its reliance on human interpretation introduces diagnostic errors. ### Key Objectives: 1. **Evaluate AI System Performance:** The study aimed to validate the ability of the AI system to analyze unedited cholangioscopy recordings and accurately classify biliary strictures as benign or malignant. 2. **Compare Diagnostic Accuracy:** The AI system's predictions were compared to traditional ERCP-based sampling techniques (brush cytology, forceps biopsy, and their combined use). 3. **Assess Generalizability:** The study examined the system's robustness across multiple institutions and diverse patient populations. ### Methods: - **Data Collection:** Cholangioscopy videos were gathered from multiple academic centers. - **AI Analysis:** The AI system processed the videos without retraining and independently generated diagnostic predictions. - **Comparison:** AI predictions were compared to diagnostic results obtained from conventional ERCP sampling methods. ### Results: - **Superior Diagnostic Accuracy:** The AI system consistently outperformed traditional ERCP-based techniques in classifying biliary strictures as benign or malignant. - **Generalizability:** The system demonstrated strong performance across different institutions and patient populations, confirming its robustness and applicability. - **Enhanced Clinical Utility:** The AI system showed potential as an adjunctive tool, improving early detection of malignancies and reducing reliance on less sensitive methods. ### Implications: 1. **Improved Diagnostic Accuracy:** AI-assisted cholangioscopy analysis significantly enhances the ability to differentiate between benign and malignant biliary strictures, addressing a critical clinical challenge. 2. **Streamlined Workflow:** The system integrates seamlessly into existing procedural workflows without requiring additional retraining or modifications. 3. **Potential for Early Detection:** By improving malignancy detection rates, the AI system could lead to earlier interventions and better patient outcomes. 4. **Reduced Diagnostic Errors:** The AI system minimizes reliance on subjective visual interpretation by clinicians, enhancing reliability. ### Conclusion: This multicenter validation study confirms that AI-based analysis of cholangioscopy footage is a powerful tool for biliary disease evaluation. Its high diagnostic accuracy, generalizability, and ability to integrate into current clinical workflows suggest that AI systems could play a transformative role in the diagnosis and management of biliary tract diseases.

AI algorithm for early identification of MASLD

The AI algorithm developed for the early identification of Metabolic Dysfunction–Associated Steatotic Liver Disease (MASLD) is a significant advancement in leveraging artificial intelligence and natural language processing (NLP) technologies to detect this prevalent yet often underdiagnosed condition. Here's a detailed explanation of the algorithm and its application: ### Purpose: The primary goal of the AI algorithm is to identify patients with MASLD early, especially since the disease is often asymptomatic in its early stages and may only become evident when it progresses to cirrhosis. Early detection can enable timely interventions, prevent disease progression, and improve patient outcomes. ### Methodology: 1. **Natural Language Processing (NLP):** - The algorithm incorporates an NLP component that scans electronic health records (EHRs), particularly abdominal imaging reports, to identify signs of hepatic steatosis (fatty liver). - It analyzes free-text imaging reports to detect mentions of hepatic steatosis with a high degree of precision. 2. **MASLD Criteria Application:** - The algorithm applies clinical criteria for MASLD, which includes excluding other causes of liver disease such as significant alcohol use. - It utilizes additional data from the EHR, such as alcohol consumption history, to ensure that identified cases meet the MASLD definition. 3. **Validation:** - The algorithm's performance was validated through manual review of patient cohorts generated monthly. - It achieved a positive predictive value (PPV) of over 93% for identifying MASLD and a PPV of up to 99.4% for detecting hepatic steatosis in imaging reports. - The algorithm also demonstrated a ~95% negative predictive value for excluding patients with alcohol use. ### Results: - Over a 6-month period, the algorithm identified 957 individuals with MASLD from EHR data. - Interestingly, only 14.6% of these patients (n=140) had a MASLD-related diagnosis code, highlighting the significant underdiagnosis of the condition in clinical practice. - This demonstrates the algorithm's potential to uncover a hidden population of patients with MASLD who might otherwise go undetected. ### Implications: - **Enhanced Detection:** The AI algorithm enables large-scale, automated screening for MASLD across healthcare systems, overcoming the limitations of manual chart reviews and reliance on diagnosis codes alone. - **Targeted Interventions:** By identifying patients earlier, healthcare providers can implement interventions such as lifestyle modifications, weight management, and monitoring to prevent disease progression. - **Adaptability:** The algorithm can be adapted by other healthcare institutions to improve MASLD detection and track alcohol use patterns in their patient populations. ### Conclusion: This AI algorithm offers a powerful tool for the early identification of MASLD with high accuracy and reliability. It addresses the challenge of underdiagnosis by leveraging EHR data and advanced NLP techniques. By integrating this tool into clinical workflows, healthcare providers can enhance MASLD detection, optimize patient care, and reduce the burden of liver disease on healthcare systems.