AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Fatty Liver

Showing 21 to 30 of 83 articles

Clear Filters

Exploring the potential of large language models in identifying metabolic dysfunction-associated steatotic liver disease: A comparative study of non-invasive tests and artificial intelligence-generated responses.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND AND AIMS: This study sought to assess the capabilities of large language models (LLMs) in identifying clinically significant metabolic dysfunction-associated steatotic liver disease (MASLD).

Identification and validation of efferocytosis-related biomarkers for the diagnosis of metabolic dysfunction-associated steatohepatitis based on bioinformatics analysis and machine learning.

Frontiers in immunology
BACKGROUND: Metabolic dysfunction-associated steatohepatitis (MASH) is a highly prevalent liver disease globally, with a significant risk of progressing to cirrhosis and even liver cancer. Efferocytosis, a process implicated in a broad spectrum of ch...

Deep Learning Based Shear Wave Detection and Segmentation Tool for Use in Point-of-Care for Chronic Liver Disease Assessments.

Ultrasound in medicine & biology
OBJECTIVE: As metabolic dysfunction-associated steatotic liver disease (MASLD) becomes more prevalent worldwide, it is imperative to create more accurate technologies that make it easy to assess the liver in a point-of-care setting. The aim of this s...

Utility of AI digital pathology as an aid for pathologists scoring fibrosis in MASH.

Journal of hepatology
BACKGROUND & AIMS: Intra and inter-pathologist variability poses a significant challenge in metabolic dysfunction-associated steatohepatitis (MASH) biopsy evaluation, leading to suboptimal selection of patients and confounded assessment of histologic...

Identification of biomarkers for the diagnosis in colorectal polyps and metabolic dysfunction-associated steatohepatitis (MASH) by bioinformatics analysis and machine learning.

Scientific reports
Colorectal polyps are precursors of colorectal cancer. Metabolic dysfunction associated steatohepatitis (MASH) is one of metabolic dysfunction associated fatty liver disease (MAFLD) phenotypic manifestations. Much evidence has suggested an associatio...

Convolutional neural network classification of ultrasound parametric images based on echo-envelope statistics for the quantitative diagnosis of liver steatosis.

Journal of medical ultrasonics (2001)
PURPOSE: Early detection and quantitative evaluation of liver steatosis are crucial. Therefore, this study investigated a method for classifying ultrasound images to fatty liver grades based on echo-envelope statistics (ES) and convolutional neural n...

Accurate non-invasive detection of MASH with fibrosis F2-F3 using a lightweight machine learning model with minimal clinical and metabolomic variables.

Metabolism: clinical and experimental
BACKGROUND: There are no known non-invasive tests (NITs) designed for accurately detecting metabolic dysfunction-associated steatohepatitis (MASH) with liver fibrosis stages F2-F3, excluding cirrhosis-the FDA-defined range for prescribing Resmetirom ...

Development of a Deep Learning Model for Classification of Hepatic Steatosis from Clinical Standard Ultrasound.

Ultrasound in medicine & biology
OBJECTIVE: Early detection and monitoring of hepatic steatosis can help establish appropriate preventative measures against progression to more advanced disease. We aimed to develop a deep learning (DL) program for classification of hepatic steatosis...

Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis.

Nature medicine
Metabolic dysfunction-associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. Manual scoring of liver biopsies, currently the gold standard for clinical trial enrollment and endp...

HepNet: Deep Neural Network for Classification of Early-Stage Hepatic Steatosis Using Microwave Signals.

IEEE journal of biomedical and health informatics
Hepatic steatosis, a key factor in chronic liver diseases, is difficult to diagnose early. This study introduces a classifier for hepatic steatosis using microwave technology, validated through clinical trials. Our method uses microwave signals and d...