AIMC Topic: Non-alcoholic Fatty Liver Disease
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Machine learning model for non-alcoholic steatohepatitis diagnosis based on ultrasound radiomics.
BMC medical imaging
Aug 20, 2024
BACKGROUND: Non-Alcoholic Steatohepatitis (NASH) is a crucial stage in the progression of Non-Alcoholic Fatty Liver Disease(NAFLD). The purpose of this study is to explore the clinical value of ultrasound features and radiological analysis in predict...
Identification and analysis of significant genes in nonalcoholic steatohepatitis-hepatocellular carcinoma transformation: Bioinformatics analysis and machine learning approach.
Molecular immunology
Aug 13, 2024
PURPOSE: Nonalcoholic steatohepatitis (NASH) has been an increasingly significant contributor to hepatocellular carcinoma (HCC). Understanding the progression from NASH to HCC is critical to early diagnosis and elucidating the underlying mechanisms.
Non-alcoholic Fatty Liver Disease
Cell Transformation, Neoplastic
Suppressor of Cytokine Signaling Proteins
Prognosis
Disease Progression
Cell Line, Tumor
Liver Neoplasms
Carcinoma, Hepatocellular
Humans
Single-Cell Analysis
Machine Learning
Computational Biology
Gene Expression Regulation, Neoplastic
AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseases.
Nature medicine
Aug 7, 2024
Clinical trials in metabolic dysfunction-associated steatohepatitis (MASH, formerly known as nonalcoholic steatohepatitis) require histologic scoring for assessment of inclusion criteria and endpoints. However, variability in interpretation has impac...
Combined structure-based virtual screening and machine learning approach for the identification of potential dual inhibitors of ACC and DGAT2.
International journal of biological macromolecules
Jul 31, 2024
Acetyl-coenzyme A carboxylase (ACC) and diacylglycerol acyltransferase 2 (DGAT2) are recognized as potential therapeutic targets for nonalcoholic fatty liver disease (NAFLD). Inhibitors targeting ACC and DGAT2 have exhibited the capacity to reduce he...
Machine learning uncovers manganese as a key nutrient associated with reduced risk of steatotic liver disease.
Liver international : official journal of the International Association for the Study of the Liver
Jul 31, 2024
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects approximately 20%-30% of the general population and is linked to high-caloric western style diet. However, there are little data that specific nutrients might help t...
Amino acid metabolomics and machine learning-driven assessment of future liver remnant growth after hepatectomy in livers of various backgrounds.
Journal of pharmaceutical and biomedical analysis
Jul 23, 2024
Accurate assessment of future liver remnant growth after partial hepatectomy (PH) in patients with different liver backgrounds is a pressing clinical issue. Amino acid (AA) metabolism plays a crucial role in liver regeneration. In this study, we comb...
Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Jun 17, 2024
Early-stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrhosis and cancer. A non-invasive and cost-effective detection method for early-stage NAFLD detection i...
Development of a diagnostic support system for the fibrosis of nonalcoholic fatty liver disease using artificial intelligence and deep learning.
The Kaohsiung journal of medical sciences
May 31, 2024
Liver fibrosis is a pathological condition characterized by the abnormal proliferation of liver tissue, subsequently able to progress to cirrhosis or possibly hepatocellular carcinoma. The development of artificial intelligence and deep learning have...
Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach.
International journal of molecular sciences
May 29, 2024
Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation...
Deep learning and digital pathology powers prediction of HCC development in steatotic liver disease.
Hepatology (Baltimore, Md.)
May 20, 2024
BACKGROUND AND AIMS: Identifying patients with steatotic liver disease who are at a high risk of developing HCC remains challenging. We present a deep learning (DL) model to predict HCC development using hematoxylin and eosin-stained whole-slide imag...
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