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

Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

Cardiomyopathy III: Hypertrophic Cardiomyopathy

509
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...
509
Cardiomyopathy II: Dilated Cardiomyopathy01:30

Cardiomyopathy II: Dilated Cardiomyopathy

588
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,...
588
Cardiomyopathy IV: Restrictive Cardiomyopathy01:29

Cardiomyopathy IV: Restrictive Cardiomyopathy

580
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...
580
Cardiomyopathy V: Interprofessional Care01:29

Cardiomyopathy V: Interprofessional Care

465
Managing cardiomyopathy involves addressing underlying or precipitating causes, treating heart failure with medications, and implementing dietary changes and a balanced exercise and rest regimen.Lifestyle ModificationsCardiomyopathy patients should adopt a low-sodium diet to reduce fluid retention and manage heart failure. A personalized exercise and rest plan helps maintain physical fitness without overstraining the heart. Avoiding alcohol and tobacco is essential to prevent further damage to...
465
Cardiomyopathy I: Introduction and Classification01:25

Cardiomyopathy I: Introduction and Classification

615
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...
615
Cardiomyopathy VI: Nursing Management01:29

Cardiomyopathy VI: Nursing Management

360
Assessment: Nursing management of patients with cardiomyopathy begins with a thorough assessment of the patient's history, including a family history of cardiomyopathy or sudden cardiac death, personal history of heart disease, hypertension, diabetes, and any alcohol consumption or drug use.During the physical examination, assess vital signs, look for signs of heart failure (such as edema, jugular venous distention, and cyanosis), auscultate for abnormal heart sounds (like murmurs and gallops),...
360

You might also read

Related Articles

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

Sort by
Same author

Fulminant early-onset subacute sclerosing panencephalitis masquerading as leukodystrophy: a diagnostic challenge.

Clinical and experimental pediatrics·2026
Same author

Continued circulation of African swine fever virus Genotype II in Mizoram, India, 2023-2025: molecular and phylogenetic evidence.

Tropical animal health and production·2026
Same author

The Unfinished Breath: Caregiver Perceptions of Terminal Events and Gaps in Amyotrophic Lateral Sclerosis Care in India.

Annals of Indian Academy of Neurology·2026
Same author

Safety and Efficacy of Carotid Stenting Without Embolic Protection Device in Delayed Window Period: Experience From A Tertiary Care Center.

Annals of Indian Academy of Neurology·2026
Same author

Paroxysmal Supraventricular Tachycardia With Wolff-Parkinson-White (WPW) Syndrome: A Therapeutic Dilemma During Pregnancy.

Cureus·2026
Same author

Intense Coughing Following Caudal Epidural Steroid Injection for Lumbar and Lumbo-Sacral Disc Prolapse with Radiculopathy. A Case Report with Pathophysiological Insights.

Journal of pain & palliative care pharmacotherapy·2026

Related Experiment Video

Updated: Feb 10, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.9K

Automated Deep Learning Based Cardiac Quantification in Hypertrophic Cardiomyopathy: A Comparative Study with Manual

Shivam Angiras1, Deb Kumar Boruah1, Pranjal Phukan1

  • 1Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences Guwahati, Assam, India.

Acta Medica Lituanica
|February 9, 2026
PubMed
Summary

Deep learning software accurately quantifies cardiac function and mitral regurgitation in Hypertrophic Cardiomyopathy (HCM) patients, matching manual assessments. This automated approach streamlines workflow but requires validation in complex cases.

Keywords:
automated segmentationcardiac MRIdeep learninghypertrophic cardiomyopathyleft ventricular functionmitral regurgitation

More Related Videos

Investigating the Pathogenesis of MYH7 Mutation Gly823Glu in Familial Hypertrophic Cardiomyopathy using a Mouse Model
03:45

Investigating the Pathogenesis of MYH7 Mutation Gly823Glu in Familial Hypertrophic Cardiomyopathy using a Mouse Model

Published on: August 8, 2022

4.3K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.9K

Related Experiment Videos

Last Updated: Feb 10, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.9K
Investigating the Pathogenesis of MYH7 Mutation Gly823Glu in Familial Hypertrophic Cardiomyopathy using a Mouse Model
03:45

Investigating the Pathogenesis of MYH7 Mutation Gly823Glu in Familial Hypertrophic Cardiomyopathy using a Mouse Model

Published on: August 8, 2022

4.3K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.9K

Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Hypertrophic Cardiomyopathy (HCM) is a common inherited heart condition.
  • Accurate assessment of Left Ventricular (LV) function and Mitral Regurgitation (MR) is vital in HCM.
  • Cardiac Magnetic Resonance (CMR) is the gold standard, but manual analysis is time-consuming.

Purpose of the Study:

  • To compare a deep learning (DL) software (SuiteHEART) against manual segmentation (syngo.Via) for cardiac parameter quantification in HCM patients.
  • To evaluate the accuracy and efficiency of automated DL-based cardiac segmentation.

Main Methods:

  • Prospective study of 25 adult HCM patients undergoing CMR.
  • Quantification of LVEF, LVEDV, LVSV, AoF, MR, and PG using both manual and automated DL segmentation.
  • Statistical analysis included correlation and Bland-Altman analysis.

Main Results:

  • Strong correlations found between DL and manual measurements for all assessed parameters (r=0.81-0.91, p<0.001).
  • Bland-Altman analysis showed acceptable agreement with no significant bias.
  • Automated segmentation significantly reduced post-processing time (p<0.001).

Conclusions:

  • Fully automated DL-based quantification accurately assesses LV function, MR, and flow in HCM patients.
  • DL algorithms can streamline clinical workflows for cardiac imaging analysis.
  • Further validation is necessary for complex HCM cases.