Gastroenterology

Noninvasive prediction of severe histopathology in drug-induced liver injury using a dual elastography-based machine learning model.

OBJECTIVES: To develop and validate a machine learning (ML) model integrating dual elastography, clinical features, and serum biomarkers for noninvasive prediction of severe drug-induced liver injury (DILI). MATERIALS AND METHODS: This prospective multicenter study enrolled consecutive DILI patients undergoing liver biopsy and dual elastography. Severe DILI was defined as Scheuer inflammation grad...

Exploring the gut-lung axis in post-liver transplant acute lung injury: A multi-omics approach.

Acute lung injury (ALI) is a significant post-operative complication of liver transplant (LT), with mounting evidence suggesting a role for the gut-lung axis. However, the mechanistic link between gut microbiota dysbiosis and ALI pathogenesis in LT recipients remains poorly understood. This hybrid translational investigation integrates transcriptomic profiling (bulk and single-cell RNA-seq), immun...

Deep Learning Predicts Mutations and Outcomes in Gastrointestinal Stromal Tumors from Whole-Slide Images.

Gastrointestinal stromal tumor (GIST) is the most common gastrointestinal mesenchymal tumor, driven by tyrosine-protein kinase (KIT) and platelet-deri...

The atlas of abdominal organ remodeling in hepatocellular carcinoma patients: An artificial intelligence-based multicenter imaging study.

BACKGROUND: Morphological changes of abdominal organs in hepatocellular carcinoma (HCC) remain uncharacterized. This study aimed to automatically quan...

Radiological Assessment of Liver and Biliary Neoplasms.

Primary liver cancer, encompassing hepatocellular carcinoma and cholangiocarcinoma, represents a growing global health burden with significant morbidi...

Integrative machine learning and multi-omics analysis reveals ATIC as a promoter of hepatocellular carcinoma progression.

Autophagy plays a non-negligible role in the progression and immune regulation of hepatocellular carcinoma (HCC). An integrated analysis of the autoph...

Deep learning layer-specific collagen quantification correlates with activity and is associated with outcomes in Crohn's disease.

BACKGROUND & AIMS: Intestinal fibrosis remains a major unmet need in inflammatory bowel disease (IBD). Although collagen deposition is the defining fe...

Metabolomic machine learning predictor for the diagnosis of alcohol-associated liver disease.

BACKGROUND: It is urgent to find novel non-invasive biomarkers that can accurately diagnose alcohol-associated liver disease (ALD). The objective of t...

Deep learning-driven inversion framework for shear modulus estimation in magnetic resonance elastography.

PURPOSE: The multimodal direct inversion (MMDI) algorithm is widely used in magnetic resonance elastography (MRE) to estimate tissue shear stiffness b...

Comment on "Development and validation of machine learning models for mortality risk stratification in acute pancreatitis patients complicated by acute kidney injury".

A histopathologically verified dataset of magnifying narrow-band imaging endoscopy for classifying the gastric precancerous cascade.

Gastric cancer is a leading cause of mortality worldwide, yet the development of computer-aided diagnosis (CAD) systems for its early detection is hin...

High-throughput identification of bacterial β-glucuronidase inhibitors using machine learning.

The human gut microbiome plays a vital role in regulating host physiological functions and influencing the pharmacokinetics of interventions, particul...

Mechanism of liquiritin from Codonopsis pilosula-Glycyrrhiza uralensis extract in alleviating piglet weaning stress via binding to alpha-1-microglobulin/bikunin precursor by machine learning and molecular dynamics simulation.

Weaning stress is a prevalent pathological condition in pig farming, primarily triggered by factors such as nutritional imbalance, oxidative stress, i...

Impact of Availability of Computer-Aided Detection Devices on Adenoma Detection During Colonoscopy: A Cluster Randomized Study.

BACKGROUND AND AIMS: Endoscopists' colonoscopy adenoma detection rates (ADR) are inversely associated with their patients' risk of post-colonoscopy co...

A Holistic Eating Monitoring System Enabled by Textile Strain Sensors and Hierarchical Network.

Wearable sensors have prompted human dynamic monitoring by quantifying physiological and biomechanical signals in daily life. However, existing approa...

AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.

BACKGROUND: Esophageal cancer (EC) remains one of the leading causes of cancer-related mortality worldwide. Accurate staging, treatment planning, and ...

A novel deep learning network for small bowel ulcerative lesion detection and differential diagnosis on double-balloon endoscopy images.

Differentiating small bowel ulcerative diseases (SBUDs) on double-balloon endoscopy (DBE) is challenging. We aimed to develop an artificial intelligen...

Computational exploration of quercetin derivatives from Azadirachta indica leaf as threonine tyrosine kinase inhibitors for potential lung and pancreatic cancer treatment.

Lung and pancreatic adenocarcinomas account for substantial cancer-related mortality worldwide, with current therapeutic efficacy severely limited by ...

Dual-stream token fusion with Swin Transformer and lesion-aware tokens for gastric metaplasia classification in IoMT-assisted deployment.

BACKGROUND: Gastric intestinal metaplasia (GIM) is often visually inconspicuous on routine endoscopy, while many artificial intelligence systems rely ...

The survival double descent: generalization dynamics of deep neural networks in time-to-event analysis.

Recent work on double descent has challenged classical bias-variance tradeoffs, showing that test error can decrease, increase sharply near the interp...