Gastroenterology

Advancing IBD Management: A Literature Review on the Role of Non-Invasive Blood-Based Biomarkers in Predicting and Assessing Pharmacodynamic Response to Treatment.

Reliable biomarkers that enable noninvasive, longitudinal assessment of disease activity and therapeutic response remain a major unmet need in inflammatory bowel disease (IBD). While colonic biopsies are the gold standard for evaluating mucosal inflammation, their invasive nature and limited spatial and temporal resolution constrain their utility in routine monitoring and clinical trials. Blood-ba...

Predictability of Same-Day Discharge Candidacy After Bariatric Surgery with Machine Learning.

INTRODUCTION: Despite growing interest, same-day discharge (SDD) after bariatric surgery remains uncommon due to challenges with patient selection. In this study, we seek to evaluate whether Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) database provides sufficient information for predicting SDD candidacy METHODS: In this retrospective cohort study, we ide...

Machine learning-enhanced plasma proteomics discriminates pancreatic cancer-associated diabetes from type 2 diabetes mellitus.

Pancreatic ductal adenocarcinoma (PDAC) is frequently preceded by new-onset diabetes mellitus (NODM), yet differentiating PDAC-associated DM from type...

The role of autophagy in ocular health: mechanisms, pathologies, and therapeutic strategies.

Autophagy is a self-digestive process in which cellular components are degraded and recycled to maintain homeostasis and cope with stress. When cells ...

Intestinal alkaline phosphatase and gut health: insights into homeostasis, barrier protection, and immune signaling.

Intestinal alkaline phosphatase (IAP) is a brush border enzyme critical for maintaining gut homeostasis by detoxifying bacterial endotoxins, regulatin...

Development of an Algorithm to Estimate Fat-Free Mass to Optimize Contrast Injection for Computed Tomography Imaging of the Liver.

OBJECTIVES: Contrast-enhanced computed tomography (CT) is central to liver imaging. Inadequate enhancement can compromise diagnostic accuracy and impa...

Machine learning outperforms large language models for survival prediction in advanced hepatocellular carcinoma: a multicenter study.

Accurate prognostic prediction remains a critical unmet need in advanced hepatocellular carcinoma (HCC). While machine learning (ML) models have demon...

A quantitative analysis of global AI medical studies: gaps in randomized controlled trials.

Medical artificial intelligence (AI) has advanced rapidly, yet a comprehensive quantitative overview of its clinical evaluation landscape remains lack...

[Current applications and prospects of artificial intelligence in perioperative comprehensive management for gastrointestinal surgery].

The shift from the traditional empirical approach to a more data-driven method in the diagnosis and treatment of GI cancers is significant due to adva...

[Machine learning models to predict intestinal necrosis in acute mesenteric venous thrombosis].

Objective: To develop and validate machine learning-based models for predicting the risk of transmural irreversible intestinal necrosis (ITIN) in pati...

Multi omics and mechanistic investigation reveals CBFB as a prognostic biomarker in gastric cancer.

BACKGROUND: Gastric cancer is an aggressive malignancy with poor prognosis due to complex pathogenesis, underscoring the need for biomarkers and targe...

A novel serum metabolite classifier for identifying Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) integrating metabolomics and machine learning.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) presents a growing global health burden, while reliable non-invasive biom...

Radiomics-based outcome prediction for irinotecan-TACE in colorectal liver metastases: advanced analysis from the prospective CIREL trial.

OBJECTIVES: Transarterial chemoembolization (TACE) is a promising locoregional therapy for unresectable colorectal liver metastases, but patient selec...

[Artificial intelligence-assisted diagnostics in the pathology of internal and oncological diseases].

BACKGROUND: Artificial intelligence (AI) is increasingly being implemented in digital pathology to support the tissue classification, cell detection, ...

Multimodal AI approach combining deep learning imaging and clinical machine learning for pancreatic cancer detection.

Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis due to late diagnosis, limitations of computed tomography (CT) imaging, and low accuracy of...

Using machine learning to identify the most important predictors of fatty liver index in healthy young Taiwanese men.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide. While many factors have been associated with NAFLD, their...

Patient-Specific Constitutive Models Based on Biaxial and Microstructural Characterisation of Fresh Human Gastric Tissue.

PURPOSE: As obesity has reached pandemic proportions worldwide, improving technical solutions for its treatment requires robust planning and numerical...

Radiologist-AI Collaboration for Ischemia Diagnosis in Small-Bowel Obstruction: Multicentric Development and External Validation of a Multimodal Deep Learning Model.

This study aims to develop and externally validate a multimodal AI model for detecting ischemia complicating small-bowel obstruction (SBO). We combine...

Performance of artificial intelligence software: EndoAngel, EndoAID, CAD-EYE, GI Genius, and EndoScreener in adenoma detection: an extended network meta-analysis.

BACKGROUND: Colorectal cancer is a major global health burden, with most cases arising from adenomatous polyps. Although colonoscopy is the gold stand...

Global Co-Occurrence Patterns of Ischemic Heart Disease and Metabolic Dysfunction-Associated Steatotic Liver Disease: Spatial Clusters, Risk Factors, and Quality-of-Care Index.

BACKGROUND: Ischemic heart disease and metabolic dysfunction-associated steatotic liver disease represent significant global health challenges, charac...