Background: Hepatocyte ballooning (HB) is a significant histological characteristic linked to the advancement of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Although clinicians now consider liver biopsy the most reliable method for identifying HB, its invasive nature and related dangers highlight the need for the development of non-invasive diagnostic options.
Objective: This study aims to develop a novel methodology that combines deep learning and machine learning techniques to accurately identify and measure hepatobiliary abnormalities in liver ultrasound images.
Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases, along with steatosis and non-alcoholic steatohepatitis (NASH), and is associated with cirrhosis and hepatocellular carcinoma. Candidate gene and genome-wide association studies have validated the relationships between NAFLD, NASH, PNPLA3, TM6SF2, and HFE. The present study utilized five polymorphisms in three genes: PNPLA3 (I148M and K434E) TM6SF2 (E167K), and HFE (H63D and C282Y), based on undocumented case−control studies in the Saudi Arabian population.
View Article and Find Full Text PDFNonalcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide. We prospectively evaluated endothelial function by assessing flow-mediated dilatation (FMD) of the brachial artery in patients with biopsy-proven NAFLD. This prospective study included 139 patients (50 healthy controls, 47 patients with steatosis and 42 patients with steatohepatitis), all of whom were nondiabetic.
View Article and Find Full Text PDFBackground/aim: Gallstone disease (GD) and nonalcoholic fatty liver disease (NAFLD) are associated with metabolic syndrome. Despite the benign nature of NAFLD, 10% of patients may develop advanced fibrosis and cirrhosis. We aimed to identify the prevalence and factors associated with NAFLD among GD patients in the Saudi population.
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