AIMC Topic: Liver Diseases

Clear Filters Showing 21 to 30 of 102 articles

Artificial intelligence for detection and characterization of focal hepatic lesions: a review.

Abdominal radiology (New York)
Focal liver lesions (FLL) are common incidental findings in abdominal imaging. While the majority of FLLs are benign and asymptomatic, some can be malignant or pre-malignant, and need accurate detection and classification. Current imaging techniques,...

Polynomial-SHAP analysis of liver disease markers for capturing of complex feature interactions in machine learning models.

Computers in biology and medicine
Liver disease diagnosis is pivotal for effective patient management, and machine learning techniques have shown promise in this domain. In this study, we investigate the impact of Polynomial-SHapley Additive exPlanations analysis on enhancing the per...

Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies.

Scientific reports
Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. Th...

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...

AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseases.

Nature medicine
Clinical trials in metabolic dysfunction-associated steatohepatitis (MASH, formerly known as nonalcoholic steatohepatitis) require histologic scoring for assessment of inclusion criteria and endpoints. However, variability in interpretation has impac...

Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches.

BMC medical informatics and decision making
PURPOSE: Liver disease causes two million deaths annually, accounting for 4% of all deaths globally. Prediction or early detection of the disease via machine learning algorithms on large clinical data have become promising and potentially powerful, b...

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.

PloS one
Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim of this study was to develop an interpretable machine learning model for early prediction of 28-day mortality in patients with SALI. Data from the Medi...

Author's reply: The importance of professional training to foster the implementation of Artificial Intelligence in the clinical setting of liver diseases.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver

Artificial intelligence in liver imaging: methods and applications.

Hepatology international
Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which exc...