Gastroenterology

Machine learning-based disease risk stratification and prediction of metabolic dysfunction-associated fatty liver disease using vibration-controlled transient elastography: Result from NHANES 2021-2023.

BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a common chronic liver disease and represents a significant public health issue. Nevertheless, current risk stratification methods remain inadequate. The study aimed to use machine learning in the identification of significant features and the development of a predictive model to determine its usefulness in discrimination ...

Uncertainty-aware segmentation quality prediction via deep learning Bayesian Modeling: Comprehensive evaluation and interpretation on skin cancer and liver segmentation.

Image segmentation is a critical step in computational biomedical image analysis, typically evaluated using metrics like the Dice coefficient during training and validation. However, in clinical settings without manual annotations, assessing segmentation quality becomes challenging, and models lacking reliability indicators face adoption barriers. To address this gap, we propose a novel framework ...

RCPE in association with the American College of Gastroenterology and the Scottish Society of Gastroenterology - Gastroenterology: A global perspective.

On 6 November 2024, the Royal College of Physicians of Edinburgh (RCPE) hosted its annual gastroenterology symposium, marking the first collaboration ...

AI-enabled opportunistic measurement of liver steatosis in coronary artery calcium scans predicts cardiovascular events and all-cause mortality: an AI-CVD study within the Multi-Ethnic Study of Atherosclerosis (MESA).

INTRODUCTION: About one-third of adults in the USA have some grade of hepatic steatosis. Coronary artery calcium (CAC) scans contain more information ...

Integrated multi-omics analysis and machine learning refine molecular subtypes and clinical outcome for hepatocellular carcinoma.

The high morbidity and mortality of hepatocellular carcinoma (HCC) impose a substantial economic burden on patients' families and society, and the maj...

The efficacy and toxicity equilibrium of emodin for liver injury: A bidirectional meta-analysis and machine learning.

BACKGROUND: Emodin, a hepatoprotective agent derived from various herbs, exhibits dual effects on liver injury, necessitating further investigation in...

Novel CT radiomics models for the postoperative prediction of early recurrence of resectable pancreatic adenocarcinoma: A single-center retrospective study in China.

PURPOSE: To assess the predictive capability of CT radiomics features for early recurrence (ER) of pancreatic ductal adenocarcinoma (PDAC).

Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review).

Esophageal cancer (EC) is one of the leading causes of cancer-related mortality worldwide, still faces significant challenges in early diagnosis and p...

Deep Learning Methods in the Imaging of Hepatic and Pancreaticobiliary Diseases.

Reports indicate a growing role for artificial intelligence (AI) in the evaluation of pancreaticobiliary and hepatic conditions. A key focus is differ...

Assessment of POPs in foods from western China: Machine learning insights into risk and contamination drivers.

Persistent organic pollutants (POPs), including PCDD/Fs, PCBs, and PBDEs, are major environmental and food safety concerns due to their bioaccumulativ...

Evaluating the dosimetric and positioning accuracy of a deep learning based synthetic-CT model for liver radiotherapy treatment planning.

An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient p...

Characterization of fibrotic liver tissue microstructure for predicting shear wave speed variability: a machine-learning-based computational study.

This study aimed to establish a link between the microstructure of simulated fibrotic liver tissues and the measured shear wave speed (SWS) variabilit...

Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods.

Early and accurate identification of patients at high risk of metabolic dysfunction-associated steatotic liver disease (MASLD) is critical to prevent ...

Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis.

Non-alcoholic fatty liver disease (NAFLD) affects about 25% of adults worldwide. Its advanced form, non-alcoholic steatohepatitis (NASH), is a major c...

A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles.

BACKGROUND: Gastric cancer (GC) is a highly heterogeneous disease, and the response of patients to clinical treatment varies substantially. There is n...

Preclinical evidence construction for epigallocatechin-3-gallate against non-alcoholic fatty liver disease: a meta-analysis and machine learning study.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) has emerged as a significant health concern worldwide, exhibiting an increasing incidence that n...

Development of an explainable prediction model for portal vein system thrombosis post-splenectomy in patients with cirrhosis.

BACKGROUND: Portal vein system thrombosis (PVST) is a common and potentially life-threatening complication following splenectomy plus pericardial deva...

Habitat Radiomics Based on MRI for Predicting Metachronous Liver Metastasis in Locally Advanced Rectal Cancer: a Two‑center Study.

RATIONALE AND OBJECTIVES: This study aimed to explore the feasibility of using habitat radiomics based on magnetic resonance imaging (MRI) to predict ...

Novel deep learning algorithm based MRI radiomics for predicting lymph node metastases in rectal cancer.

To explore the value of applying the MRI-based radiomic nomogram for predicting lymph node metastasis (LNM) in rectal cancer (RC). This retrospective ...

Comprehensive analysis of scRNA-seq and bulk RNA-seq data via machine learning and bioinformatics reveals the role of lysine metabolism-related genes in gastric carcinogenesis.

BACKGROUND: Gastric cancer (GC) is a highly aggressive and heterogeneous cancer with extremely complex biological characteristics. Lysine and its meta...