Gastroenterology

IBDome: An integrated molecular, histopathological, and clinical atlas of inflammatory bowel diseases.

BACKGROUND & AIMS: Multi-omic and multimodal datasets with detailed clinical annotations offer significant potential to advance our understanding of inflammatory bowel diseases (IBD), refine diagnostics, and enable personalized therapeutic strategies. METHODS: In this multi-cohort study, we performed an extensive multi-omic and multimodal analysis of 1,002 clinically annotated patients with IBD an...

A Scoping Review of Endoscopic Assessment Considering Disease Extent in Ulcerative Colitis: Insights for the Artificial Intelligence Era.

Conventional endoscopic indices for ulcerative colitis (UC) primarily assess the most severely affected segment, potentially underestimating the cumulative inflammatory burden across the colon. Consequently, there is increasing interest in assessment methods that incorporate both disease severity and spatial extent, including emerging artificial intelligence (AI)-based approaches. The aim of this ...

Data-Informed Intuition in Hepatology: Integrating Evidence, Context, and Clinical Reasoning.

Artificial intelligence and data-driven models are changing hepatology, but expert clinical judgment remains essential. Liver diseases are complex and...

Quantification of Histotripsy Dosage Using Machine Learning-Enhanced Ultrasound Imaging Analysis: Correlation with Histological Outcomes.

RATIONALE AND OBJECTIVES: Histotripsy is a noninvasive ultrasound therapy that mechanically disrupts target tissue through controlled acoustic cavitat...

Differentiating malignancy from liver parenchyma in Ex-Vivo OCT images using anomaly detection.

Primary liver cancer and colorectal liver metastases (CRLM) pose significant challenges, because of limited early diagnosis and the reliance on time-c...

AI-assisted improvement of endoscopic diagnosis of ampullary lesions.

Ampullary neoplasms are often challenging to detect during forward-viewing esophagogastroduodenoscopy. This study aimed to evaluate the impact of arti...

Efficient colorectal cancer classification from histopathological images utilizing lightweight convolutional neural networks.

As one of the most fatal malignancies worldwide, colorectal cancer demands accurate histopathological image analysis to support timely diagnosis and e...

Prediction of ICU length of stay, hospital discharge outcomes and discharge location among ICU-admitted patients diagnosed with viral hepatitis using machine learning: a retrospective cohort study of the MIMIC-IV database.

BACKGROUND: Hepatitis, a disease characterised by inflammation of the liver, is a leading global health challenge that contributes to over 1.3 million...

LightGastroFormer: a lightweight multi-resolution transformer for gastrointestinal disease classification.

In order to promote early diagnosis and lessen the workload of doctors during endoscopic and capsule endoscopy tests, automated analysis of gastrointe...

Artificial Intelligence Applications in Endoscopic Sleeve Gastroplasty: A Systematic Review of Preliminary Evidence.

INTRODUCTION: Endoscopic Sleeve Gastroplasty (ESG) is an established minimally invasive bariatric intervention, while artificial intelligence (AI) has...

Comparative evaluation and selection of optimal QSAR-based machine learning model for liver toxicity prediction.

Drug induced liver toxicity remains the most common cause of acute liver failure. Conventional toxicity detection relies on resource-intensive in vivo...

mFLIP: metabolic flux interval prediction.

BACKGROUND: Understanding cellular metabolism often involves an accurate estimation of metabolic fluxes-the rates at which metabolites are converted i...

Identifying communities at elevated risk of hepatitis C infection in Los Angeles County: an ecological study using machine learning.

BACKGROUND: Identifying communities disproportionately affected by hepatitis C infection is essential for targeted prevention and resource allocation....

Multi-omics reveals fibroblast subtypes with distinct roles in inflammatory bowel disease.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract involving complex interactions among epi...

A Novel Blood-Based Screening Test for Colorectal Cancer Using Raman Spectroscopy.

BACKGROUND: Label-free vibrational spectroscopic techniques (Raman spectroscopy) combined with machine learning (ML) methodologies have huge potential...

Artificial intelligence in inflammatory bowel disease: From current evidence, clinical translation, and the road to precision medicine.

Artificial intelligence (AI) is rapidly transforming healthcare, supporting disease management and enabling outcome prediction across multiple clinica...

Development of a Computed Tomography Enterography Based Radiomics Model to Characterize Inflammation, Fibrosis and Smooth Muscle Thickening in Stricturing Crohn's Disease.

BACKGROUND AIMS: Computed tomography enterography (CTE) is a non-invasive cross-sectional imaging modality routinely used for diagnosis of Crohn's dis...

Fully automated detection of pancreaticobiliary maljunction based on 3D magnetic resonance cholangiopancreatography using deep learning.

INTRODUCTION: Pancreaticobiliary maljunction (PBM) is closely related to biliary tract cancer. The early detection of PBM is crucial but challenging. ...

A multidimensional strategy for identifying quality markers of Radix ginseng-Schisandra chinensis in the disruption of the inflammation-cancer transformation process of hepatocellular carcinoma based on metabolic regulation and chemical properties.

Comprehensive quality control is essential for ensuring the efficacy and safety of traditional Chinese medicines (TCMs). However, current quality cont...

Machine learning model to identify gut microbiome-derived metabolites as potential biomarkers of autism spectrum disorder: a pilot study.

Autism Spectrum Disorder (ASD) arises from complex and not yet completely understood interactions between genetic and environmental factors. Alongside...