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Related Concept Videos

Cirrhosis I: Introduction01:23

Cirrhosis I: Introduction

Cirrhosis is a chronic, irreversible liver disease characterized by the widespread replacement of healthy liver tissue with fibrotic scar tissue and the formation of regenerative nodules.Etiology of cirrhosisCirrhosis results from sustained liver injury that triggers progressive fibrosis and structural remodeling. The underlying causes are diverse, encompassing common and less frequent clinical conditions. Regardless of the origin, all causes lead to chronic inflammation, hepatocyte loss, and...
Cirrhosis II: Pathophysiology01:24

Cirrhosis II: Pathophysiology

Cirrhosis is a progressive chronic liver injury caused by prolonged inflammation, excessive fibrotic remodeling, and impaired regeneration. Over time, repeated hepatic insults disrupt the liver’s architecture and function, leading to reduced blood flow, impaired bile drainage, and diminished metabolic capacity.Pathophysiology of cirrhosisCirrhosis arises from three main responses to chronic liver damage: inflammation, immune activation, and hepatocyte death. These processes lead to structural...
Liver Regeneration01:24

Liver Regeneration

The liver is an important organ in vertebrates that plays an essential role in metabolism. It is also responsible for storing and redistributing nutrients such as carbohydrates, fats, and vitamins in the body. Additionally, the liver releases bile salts which are critical for digesting food and eliminating toxic metabolites from the body.
Cells of Liver
The liver comprises four major types of cells— hepatocytes, stellate, Kupffer, and sinusoidal endothelial cells. The hepatocytes are large...
Diseases of the Liver and Gallbladder01:26

Diseases of the Liver and Gallbladder

Liver and gallbladder diseases are a significant health concern, with prominent conditions including cirrhosis, hepatitis, non-alcoholic fatty liver disease (NAFLD), and gallstones. Jaundice is a common manifestation of liver and biliary disease.
Cirrhosis is characterized by the scarring of hepatic lobules in the liver, which are replaced by fibrous tissue, affecting the liver's normal functioning. NAFLD, on the other hand, is caused by an excessive build-up of fat in the liver, not related to...

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Updated: May 26, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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LC-Pred: A Transformer-Based Interactive Interface for Liver Cirrhosis Prediction.

Bisweswari Rath1, Satya Ranjan Dash2, Rajani Kanta Mahapatra1

  • 1School of Biotechnology, KIIT Deemed To Be University, Bhubaneswar, India, kiit.ac.in.

International Journal of Hepatology
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning model, LC-Pred, accurately predicts liver cirrhosis (LC) using patient data. This AI tool offers early detection, aiding clinicians and improving patient outcomes in hepatology.

Keywords:
AIChild–PughLCMELDclinical datahepatologyliver cirrhosisnoninvasive diagnosistransformer modelweb application

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Area of Science:

  • Hepatology and Artificial Intelligence
  • Deep Learning in Clinical Diagnostics
  • Medical Informatics

Background:

  • Liver cirrhosis (LC) poses a significant health burden, necessitating early and accurate diagnostic tools.
  • Traditional scoring systems for LC assessment have limitations in accuracy and clinical workflow integration.
  • Advancements in artificial intelligence (AI) offer potential for improved diagnostic capabilities in healthcare.

Purpose of the Study:

  • To develop and validate an AI-based tool, LC-Pred, for the early prediction of liver cirrhosis.
  • To compare the performance of a deep learning model against traditional scoring systems for LC detection.
  • To create a user-friendly web application for seamless integration into clinical practice.

Main Methods:

  • Utilized a dataset of 1098 patients with liver cirrhosis (LC) and non-liver cirrhosis (NLC) cases.
  • Employed a transformer deep learning model, analyzing 20 clinical parameters and demographic data (age, gender).
  • Developed a client-server web application (LC-Pred) with authentication and report generation features.

Main Results:

  • The transformer model achieved high performance metrics: PR-AUC of 0.907, ROC-AUC of 0.989, sensitivity of 0.857, specificity of 0.947, and test accuracy of 0.977.
  • LC-Pred demonstrated superior accuracy compared to traditional scoring systems like MELD and Child-Pugh.
  • The developed tool offers both single and bulk prediction capabilities, enhancing its utility in healthcare.

Conclusions:

  • LC-Pred represents a significant advancement in AI-assisted hepatology, offering improved clinical efficacy over traditional methods.
  • The tool's user-friendly interface and robust performance facilitate its adoption in clinical workflows for early LC detection.
  • Implementing aligned AI systems like LC-Pred is crucial for enhancing diagnostic accuracy and patient care in liver disease management.