AIMC Topic: Liver Cirrhosis

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Generative AI in hepatology: Transforming multimodal patient-generated data into actionable insights.

Hepatology communications
Cirrhosis care is inherently complex, marked by a high risk of acute decompensation and significant morbidity and mortality. Traditional episodic care models provide static snapshots of a patient's condition, limiting the ability to address dynamic c...

Cost-effectiveness analysis of artificial intelligence (AI) in earlier detection of liver lesions in cirrhotic patients at risk of hepatocellular carcinoma in Italy.

Journal of medical economics
BACKGROUND: Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide and the third most common cause of cancer-related death. Cirrhosis is a major contributing factor, accounting for over 90% of HCC cases. With the high mortality rate...

Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.

Scientific reports
Advanced metabolic-dysfunction-associated steatotic liver disease (MASLD) fibrosis (F3-4) predicts liver-related outcomes. Serum and elastography-based non-invasive tests (NIT) cannot yet reliably predict MASLD outcomes. The role of B-mode ultrasound...

Gut microbiome alterations and hepatic encephalopathy post-TIPS in liver cirrhosis patients.

Journal of translational medicine
BACKGROUND: The transjugular intrahepatic portosystemic shunt (TIPS), a crucial tool for treating complications related to portal hypertension in patients with liver cirrhosis, is often associated with an increased risk of postoperative complications...

Therapeutic horizons in metabolic dysfunction-associated steatohepatitis.

The Journal of clinical investigation
Metabolic dysfunction-associated steatohepatitis (MASH), the progressive inflammatory form of MASLD, is now a leading cause of chronic liver disease worldwide. Driven by obesity and type 2 diabetes, MASH significantly increases the risk of cirrhosis,...

Automated quantification of T1 and T2 relaxation times in liver mpMRI using deep learning: a sequence-adaptive approach.

European radiology experimental
OBJECTIVES: To evaluate a deep learning sequence-adaptive liver multiparametric MRI (mpMRI) assessment with validation in different populations using total and segmental T1 and T2 relaxation time maps.

A plasma metabolome-derived model predicts severe liver outcomes of nonalcoholic fatty liver disease in the UK Biobank.

Diabetes, obesity & metabolism
AIMS: Severe liver disease (SLD) in nonalcoholic fatty liver disease (NAFLD) is often diagnosed late due to the long asymptomatic period of progressive fibrosis. We aimed to identify metabolomic profiles associated with SLD and develop a predictive m...

Large Scale MRI Collection and Segmentation of Cirrhotic Liver.

Scientific data
Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly increases mortality risk. While magnetic resonance imaging (MRI) offers a non-invasive assessment, accu...

Nucleic acid spheres for treating capillarisation of liver sinusoidal endothelial cells in liver fibrosis.

Nature communications
Liver sinusoidal endothelial cells (LSECs) lose their characteristic fenestrations and become capillarized during the progression of liver fibrosis. Mesenchymal stem cell (MSC) transplantation can reverse this capillarization and reduce fibrosis, but...