Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

541
Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
541
Cardiomyopathy I: Introduction and Classification01:25

Cardiomyopathy I: Introduction and Classification

826
Cardiomyopathy, or CMP, is a group of diseases affecting the myocardial structure, impairing its ability to pump blood effectively. This condition can lead to arrhythmias, heart failure, or sudden cardiac death.Cardiomyopathies are classified into primary and secondary categories:Primary Cardiomyopathy refers to conditions involving only the heart muscle that are often idiopathic (of unknown cause) or genetic. They primarily affect the myocardium without the involvement of other systemic...
826
Cardiomyopathy II: Dilated Cardiomyopathy01:30

Cardiomyopathy II: Dilated Cardiomyopathy

790
Dilated cardiomyopathy, or DCM, is a progressive myocardial disorder characterized by ventricular chamber dilation and contractile dysfunction.EtiologyVarious factors can cause DCM, including hypertension and heavy alcohol intake, which contribute to the weakening and enlargement of the heart muscle. Viral infections, such as Coxsackievirus B, adenoviruses, and influenza, can lead to DCM by causing inflammation and damage to heart tissue. Certain chemotherapeutic agents, including daunorubicin,...
790
Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

Cardiomyopathy III: Hypertrophic Cardiomyopathy

805
Hypertrophic cardiomyopathy, or HCM, is an autosomal dominant genetic disorder characterized by asymmetric left ventricular hypertrophy without ventricular dilation. It is more common in men and is typically diagnosed in young, athletic adults.EtiologyHCM is primarily genetic and is caused by mutations in genes encoding sarcomeric proteins. Researchers have identified over 1400 mutations across at least 11 different genes. Among these, the most frequently occurring mutations are found in the...
805
Cardiomyopathy IV: Restrictive Cardiomyopathy01:29

Cardiomyopathy IV: Restrictive Cardiomyopathy

952
Restrictive cardiomyopathy (RCM) is a rare heart muscle disease characterized by impaired ventricular filling due to stiffened ventricular walls, leading to significant diastolic dysfunction.EtiologyRestrictive cardiomyopathy can arise from both inherited and acquired diseases, many of which are systemic. It is categorized into four main types: infiltrative, storage, non-infiltrative, and endomyocardial diseases.Infiltrative diseases, such as amyloidosis, lead to RCM by depositing amyloid...
952

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Automated FDG uptake/PET-CT fused scan diagnosis of various lymph node tumors using object detection AI techniques.

Scientific reports·2026
Same author

Explainable Split-Learning-Based Framework for Accurate Pulmonary Nodule Classification.

Bioengineering (Basel, Switzerland)·2026
Same author

Theoretical analysis for heat and mass transfer in bioconvective ternary hybrid nanofluid flow with entropy optimization.

Discover nano·2026
Same author

Different BI-RADS breast cancer diagnosis using MobileNetV1 and vision transformer based on explainable artificial intelligence (XAI).

Scientific reports·2026
Same author

A Hybrid Deep Learning Framework for Automated Dental Disorder Diagnosis from X-Ray Images.

Journal of clinical medicine·2026
Same author

Enhancing automatic diagnosis of thyroid nodules from ultrasound scans leveraging deep learning models.

Scientific reports·2025

Related Experiment Video

Updated: May 5, 2026

Scanning Electron Microscopy of Macerated Tissue to Visualize the Extracellular Matrix
10:21

Scanning Electron Microscopy of Macerated Tissue to Visualize the Extracellular Matrix

Published on: June 14, 2016

10.0K

Cardiac Fibrosis Automated Diagnosis Based on FibrosisNet Network Using CMR Ischemic Cardiomyopathy.

Mohamed Bekheet1,2, Mohammed Sallah3, Norah S Alghamdi4

  • 1Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt.

Diagnostics (Basel, Switzerland)
|February 10, 2024
PubMed
Summary

Early detection of heart muscle fibrosis is crucial for improving outcomes in ischemic heart disease. A new deep learning model, FibrosisNet, accurately identifies and classifies cardiac fibrosis using magnetic resonance imaging, enhancing diagnostic capabilities.

Keywords:
CMRFibrosisNetLGEdeep learningfibrosismagnetic resonance imaging

More Related Videos

Refined CLARITY-Based Tissue Clearing for Three-Dimensional Fibroblast Organization in Healthy and Injured Mouse Hearts
07:10

Refined CLARITY-Based Tissue Clearing for Three-Dimensional Fibroblast Organization in Healthy and Injured Mouse Hearts

Published on: May 16, 2021

4.7K
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.2K

Related Experiment Videos

Last Updated: May 5, 2026

Scanning Electron Microscopy of Macerated Tissue to Visualize the Extracellular Matrix
10:21

Scanning Electron Microscopy of Macerated Tissue to Visualize the Extracellular Matrix

Published on: June 14, 2016

10.0K
Refined CLARITY-Based Tissue Clearing for Three-Dimensional Fibroblast Organization in Healthy and Injured Mouse Hearts
07:10

Refined CLARITY-Based Tissue Clearing for Three-Dimensional Fibroblast Organization in Healthy and Injured Mouse Hearts

Published on: May 16, 2021

4.7K
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.2K

Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Ischemic heart disease is a leading cause of mortality, with early identification improving treatment efficacy and survival rates.
  • Heart muscle fibrosis, a key factor in ischemic heart disease, impairs cardiac function and is associated with adverse cardiovascular events.
  • Cardiac magnetic resonance (CMR) imaging is vital for detecting fibrosis, a significant risk factor for ischemic heart disease.

Purpose of the Study:

  • To introduce a novel deep learning (DL) network, FibrosisNet, for the accurate detection and classification of heart muscle fibrosis.
  • To evaluate the performance of FibrosisNet in diagnosing cardiac fibrosis using magnetic resonance imaging (MRI) data.
  • To compare FibrosisNet's efficacy against existing state-of-the-art methods and advanced convolutional neural network (CNN) approaches.

Main Methods:

  • Development of FibrosisNet, a deep network incorporating 17 series layers for fibrosis detection.
  • Training and evaluation of the FibrosisNet classification system for optimal performance.
  • Application of deep transfer learning models on established CNN architectures for fibrosis detection.

Main Results:

  • FibrosisNet achieved high performance metrics: 96.05% accuracy, 97.56% sensitivity, and 96.54% F1-Score.
  • The study demonstrated FibrosisNet's superior performance compared to current state-of-the-art methods.
  • Experimental results confirmed the effectiveness of FibrosisNet in detecting and classifying cardiac fibrosis.

Conclusions:

  • FibrosisNet represents a significant advancement in the early and accurate diagnosis of heart muscle fibrosis.
  • The proposed DL model offers substantial medical benefits by improving therapeutic outcomes and patient survival rates in ischemic heart disease.
  • FibrosisNet shows promise as a valuable tool in clinical practice for managing patients with cardiac fibrosis.