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Liver Cirrhosis

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Automatic liver segmentation and assessment of liver fibrosis using deep learning with MR T1-weighted images in rats.

Magnetic resonance imaging
OBJECTIVES: To validate the performance of nnU-Net in segmentation and CNN in classification for liver fibrosis using T1-weighted images.

Integrated Bioinformatics and Validation Reveal and Its Related Molecules as Potential Identifying Genes in Liver Cirrhosis.

Biomolecules
Liver cirrhosis remains a significant global public health concern, with liver transplantation standing as the foremost effective treatment currently available. Therefore, investigating the pathogenesis of liver cirrhosis and developing novel therapi...

Artificial intelligence scoring of liver biopsies in a phase II trial of semaglutide in nonalcoholic steatohepatitis.

Hepatology (Baltimore, Md.)
BACKGROUND AND AIMS: Artificial intelligence-powered digital pathology offers the potential to quantify histological findings in a reproducible way. This analysis compares the evaluation of histological features of NASH between pathologists and a mac...

Deep learning quantification reveals a fundamental prognostic role for ductular reaction in biliary atresia.

Hepatology communications
BACKGROUND: We aimed to quantify ductular reaction (DR) in biliary atresia using a neural network in relation to underlying pathophysiology and prognosis.

Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005-2023).

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Non-alcoholic fatty liver disease (NAFLD) is a common liver disease with a rapidly growing incidence worldwide. For prognostication and therapeutic decisions, it is important to distinguish the pathological stages of NAFLD:...

Liver fibrosis classification from ultrasound using machine learning: a systematic literature review.

Abdominal radiology (New York)
PURPOSE: Liver biopsy was considered the gold standard for diagnosing liver fibrosis; however, with advancements in medical technology and increasing awareness of potential complications, the reliance on liver biopsy has diminished. Ultrasound is gai...

An uncertainty aided framework for learning based livermapping and analysis.

Physics in medicine and biology
. QuantitativeT1ρimaging has potential for assessment of biochemical alterations of liver pathologies. Deep learning methods have been employed to accelerate quantitativeT1ρimaging. To employ artificial intelligence-based quantitative imaging methods...

Curcumin Modulates NOX Gene Expression and ROS Production via P-Smad3C in TGF-β-Activated Hepatic Stellate Cells.

Iranian biomedical journal
BACKGROUND: Liver fibrosis, associated with hepatic stellate cells (HSCs), occurs when a healthy liver sustains damage, thereby impairing its function. NADPH oxidases (NOXs), specifically isoforms 1, 2, and 4, play a role in reactive oxygen species (...

Shear wave elastography-based deep learning model for prognosis of patients with acutely decompensated cirrhosis.

Journal of clinical ultrasound : JCU
PURPOSE: This study aimed to develop and validate a deep learning model based on two-dimensional (2D) shear wave elastography (SWE) for predicting prognosis in patients with acutely decompensated cirrhosis.

Using Artificial Intelligence to Predict Cirrhosis From Computed Tomography Scans.

Clinical and translational gastroenterology
INTRODUCTION: Undiagnosed cirrhosis remains a significant problem. In this study, we developed and tested an automated liver segmentation tool to predict the presence of cirrhosis in a population of patients with paired liver biopsy and computed tomo...