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Fatty Liver

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Development and validation of machine learning models for MASLD: based on multiple potential screening indicators.

Frontiers in endocrinology
BACKGROUND: Multifaceted factors play a crucial role in the prevention and treatment of metabolic dysfunction-associated steatotic liver disease (MASLD). This study aimed to utilize multifaceted indicators to construct MASLD risk prediction machine l...

Golgi protein 73: charting new territories in diagnosing significant fibrosis in MASLD: a prospective cross-sectional study.

Frontiers in endocrinology
OBJECTIVES: To explore the correlation between serum Golgi protein 73 (GP73) levels and the degree of fibrosis in Metabolic dysfunction associated steatotic liver disease (MASLD); to establish a non-invasive diagnostic algorithm based on serum GP73 a...

Artificial Intelligence-Based Diets: A Role in the Nutritional Treatment of Metabolic Dysfunction-Associated Steatotic Liver Disease?

Journal of human nutrition and dietetics : the official journal of the British Dietetic Association
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing global health concern. Effective management of this condition relies heavily on lifestyle modifications and dietary interventions. In this study, we sought to e...

Ultrasound Versus Elastography in the Diagnosis of Hepatic Steatosis: Evaluation of Traditional Machine Learning Versus Deep Learning.

Sensors (Basel, Switzerland)
The prevalence of fatty liver disease is on the rise, posing a significant global health concern. If left untreated, it can progress into more serious liver diseases. Therefore, accurately diagnosing the condition at an early stage is essential for m...

Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT.

PloS one
PURPOSE: This study aims to explore the potential of non-contrast abdominal CT radiomics and deep learning models in accurately diagnosing fatty liver.

Assessment of ChatGPT-generated medical Arabic responses for patients with metabolic dysfunction-associated steatotic liver disease.

PloS one
BACKGROUND AND AIM: Artificial intelligence (AI)-powered chatbots, such as Chat Generative Pretrained Transformer (ChatGPT), have shown promising results in healthcare settings. These tools can help patients obtain real-time responses to queries, ens...

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.

FibrAIm - The machine learning approach to identify the early stage of liver fibrosis and steatosis.

International journal of medical informatics
BACKGROUND: Early recognition of steatosis (fatty liver) and fibrosis in liver health is crucial for effectively managing and preventing the possibility of liver dysfunction. Detecting steatosis helps identify individuals at risk of liver-related dis...

Automated identification of incidental hepatic steatosis on Emergency Department imaging using large language models.

Hepatology communications
BACKGROUND: Hepatic steatosis is a precursor to more severe liver disease, increasing morbidity and mortality risks. In the Emergency Department, routine abdominal imaging often reveals incidental hepatic steatosis that goes undiagnosed due to the ac...

Trans-ancestral rare variant association study with machine learning-based phenotyping for metabolic dysfunction-associated steatotic liver disease.

Genome biology
BACKGROUND: Genome-wide association studies (GWAS) have identified common variants associated with metabolic dysfunction-associated steatotic liver disease (MASLD). However, rare coding variant studies have been limited by phenotyping challenges and ...