AIMC Topic: Liver Cirrhosis

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Utility of AI digital pathology as an aid for pathologists scoring fibrosis in MASH.

Journal of hepatology
BACKGROUND & AIMS: Intra and inter-pathologist variability poses a significant challenge in metabolic dysfunction-associated steatohepatitis (MASH) biopsy evaluation, leading to suboptimal selection of patients and confounded assessment of histologic...

Machine learning algorithms reveal gut microbiota signatures associated with chronic hepatitis B-related hepatic fibrosis.

World journal of gastroenterology
BACKGROUND: Hepatic fibrosis (HF) represents a pivotal stage in the progression and potential reversal of cirrhosis, underscoring the importance of early identification and therapeutic intervention to modulate disease trajectory.

Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data.

Clinical transplantation
BACKGROUND AND AIM: Liver transplant (LT) recipients may succumb to graft-related pathologies, contributing to graft fibrosis (GF). Current methods to diagnose GF are limited, ranging from procedural-related complications to low accuracy. With recent...

Machine learning-based models for advanced fibrosis in non-alcoholic steatohepatitis patients: A cohort study.

World journal of gastroenterology
BACKGROUND: The global prevalence of non-alcoholic steatohepatitis (NASH) and its associated risk of adverse outcomes, particularly in patients with advanced liver fibrosis, underscores the importance of early and accurate diagnosis.

Application of Interpretable Machine Learning Models to Predict the Risk Factors of HBV-Related Liver Cirrhosis in CHB Patients Based on Routine Clinical Data: A Retrospective Cohort Study.

Journal of medical virology
Chronic hepatitis B (CHB) infection represents a significant global public health issue, often leading to hepatitis B virus (HBV)-related liver cirrhosis (HBV-LC) with poor prognoses. Early identification of HBV-LC risk is essential for timely interv...

Identification of hub gene for the pathogenic mechanism and diagnosis of MASLD by enhanced bioinformatics analysis and machine learning.

PloS one
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a heterogeneous disease caused by multiple etiologies. It is characterized by excessive fat accumulation in the liver. Without intervention, MASLD can progress from steatosis to meta...

Exploring shared pathogenic mechanisms and biomarkers in hepatic fibrosis and inflammatory bowel disease through bioinformatics and machine learning.

Frontiers in immunology
BACKGROUND: The coexistence of hepatic fibrosis (HF) and inflammatory bowel disease (IBD) represents a significant clinical concern due to their poorly characterized shared pathogenic mechanisms. Current limitations in identifying common biomarkers f...

Identification of regulatory cell death-related genes during MASH progression using bioinformatics analysis and machine learning strategies.

Frontiers in immunology
BACKGROUND: Metabolic dysfunction-associated steatohepatitis (MASH) is becoming increasingly prevalent. Regulated cell death (RCD) has emerged as a significant disease phenotype and may act as a marker for liver fibrosis. The present study aimed to i...

MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.

Briefings in bioinformatics
The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the microbiota as a promising indicator or predictor with machine learning (ML) methods. However, the a...

Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis.

Briefings in bioinformatics
We sought to develop and validate a machine learning (ML) model for predicting multidimensional frailty based on clinical and laboratory data. Moreover, an explainable ML model utilizing SHapley Additive exPlanations (SHAP) was constructed. This stud...