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

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

481
Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
481
Assessment of the Cardiovascular System I: Subjective Data01:23

Assessment of the Cardiovascular System I: Subjective Data

566
A thorough health history and physical assessment are essential for identifying cardiovascular disease (CVD) symptoms and distinguishing them from other health issues.
Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...
566
Pathophysiology of Cardiac Performance01:29

Pathophysiology of Cardiac Performance

987
Typical heart performance is influenced by heart rate, rhythm, myocardial contraction, and metabolism or blood flow. The cardiac muscle exhibits distinct electrophysiological features, including pacemaker activity and calcium channel control, which play a vital role in the heart's response to various drugs. The autonomic nervous system, comprising the sympathetic and parasympathetic branches, regulates heart rate. Sympathetic activation increases heart rate, while parasympathetic activation...
987

You might also read

Related Articles

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

Sort by
Same author

Editorial: Brain connectomics: a comprehensive mapping and analysis of brain connectivity in health and disease.

Frontiers in medicine·2026
Same author

A Method for Workout Video Classification via Explainable and Federated Learning.

Bioengineering (Basel, Switzerland)·2026
Same author

Analyzing Gait Pattern Associated With Neuropsychiatric Symptoms in Parkinson's Disease by a Comprehensive Approach.

IEEE journal of translational engineering in health and medicine·2026
Same author

On Vision Transformer Explainability for Personal Protective Equipment Detection: A Qualitative and Quantitative Analysis.

Journal of imaging·2026
Same author

Telemedicine and 5G Technologies: A Systematic Global Review of Applications over the Past Decade.

Bioengineering (Basel, Switzerland)·2026
Same author

A Novel Convolutional Neural Network for Explainable Diabetic Retinopathy Detection and Grade Identification.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: Nov 7, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K

Machine learning analysis: general features, requirements and cardiovascular applications.

Carlo Ricciardi1,2, Renato Cuocolo3, Rosario Megna4

  • 1Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy - carloricciardi.93@gmail.com.

Minerva Cardiology and Angiology
|May 4, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) offer solutions for complex medical challenges. Standardization of ML analysis reporting is crucial for its integration into clinical practice.

More Related Videos

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.0K
Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
11:13

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

Published on: May 24, 2021

6.7K

Related Experiment Videos

Last Updated: Nov 7, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K
In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.0K
Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
11:13

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

Published on: May 24, 2021

6.7K

Area of Science:

  • Medicine
  • Computer Science

Background:

  • Artificial intelligence (AI) is poised to transform medicine.
  • Machine learning (ML) and deep learning are key AI subfields.
  • Understanding ML subgroups and the traditional analysis process is essential.

Purpose of the Study:

  • Discuss general features of machine learning (ML).
  • Highlight the need for standardized reporting of ML analysis results.
  • Explore pathways for ML integration into clinical practice.

Main Methods:

  • Review AI, ML, and deep learning classifications.
  • Describe the typical ML analysis workflow: data collection, feature engineering, modeling, validation, and deployment.
  • Identify and discuss proposed standards for reporting ML results.

Main Results:

  • ML analysis involves distinct stages from data collection to deployment.
  • Lack of standardized reporting hinders ML adoption in medicine.
  • Key reporting standards include study population, repeatability, validation, results, and comparison with current practice.

Conclusions:

  • Standardized reporting is necessary for reliable ML application in healthcare.
  • Advancements in technology and computational tools will facilitate ML use in clinical settings.
  • The integration of ML promises improved patient care and assistance strategies.