AIMC Topic: Fatty Liver

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Identification and Validation of Biomarkers in Metabolic Dysfunction-Associated Steatohepatitis Using Machine Learning and Bioinformatics.

Molecular genetics & genomic medicine
BACKGROUND: The incidence of metabolic dysfunction-associated steatohepatitis (MASH) is increasing annually. MASH can progress to cirrhosis and hepatocellular carcinoma. However, the early diagnosis of MASH is challenging.

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...

Machine Learning Reveals the Contribution of Lipoproteins to Liver Triglyceride Content and Inflammation.

The Journal of clinical endocrinology and metabolism
CONTEXT: Metabolic dysfunction-associated steatotic liver disease (MASLD) is currently the most common chronic liver disease worldwide and is strongly associated with metabolic comorbidities, including dyslipidemia.

Machine Learning Identifies Metabolic Dysfunction-Associated Steatotic Liver Disease in Patients With Diabetes Mellitus.

The Journal of clinical endocrinology and metabolism
CONTEXT: The presence of metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with diabetes mellitus (DM) is associated with a high risk of cardiovascular disease, but is often underdiagnosed.

Liver fat analysis using optimized support vector machine with support vector regression.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Fatty liver disease is a common condition caused by excess fat in the liver. It consists of two types: Alcoholic Fatty Liver Disease, also called alcoholic steatohepatitis, and Non-Alcoholic Fatty Liver Disease (NAFLD). As per epidemiolog...

The quest for the missing links in fatty liver genetics: Deep learning to the rescue!

Cell reports. Medicine
Park, MacLean, et al. conduct an exome-wide association study of liver fat content in the Penn Medicine BioBank. By leveraging machine learning-assisted analysis of clinical CT scans to quantify steatosis, they uncover previously undescribed liver fa...

Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images scalable deep learning.

World journal of gastroenterology
BACKGROUND: Hepatic steatosis is a major cause of chronic liver disease. Two-dimensional (2D) ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated diagnoses are highly subjective.

Applications of Machine Learning in Fatty Live Disease Prediction.

Studies in health technology and informatics
: Fatty liver disease (FLD) is considered the most prevalent form of chronic liver disease worldwide. The prediction of fatty liver disease is an important factor for effective treatment and reduce serious health consequences. We, therefore construct...