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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and the T...
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

Cardiomyopathy III: Hypertrophic Cardiomyopathy

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...
Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
Aortic Regurgitation II: Clinical Features and Diagnostic Tests01:22

Aortic Regurgitation II: Clinical Features and Diagnostic Tests

Aortic valve regurgitation (AR) occurs when the aortic valve fails to close properly, allowing blood to flow backward from the aorta into the left ventricle. This backflow can result in two distinct clinical presentations: acute and chronic AR, each characterized by its own set of symptoms and physical findings.Acute Aortic RegurgitationAcute AR presents with a sudden onset of severe symptoms. Patients typically experience profound dyspnea (shortness of breath), chest pain, and signs of left...

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Related Experiment Video

Updated: Jul 4, 2026

Pulsed Wave Doppler Assessment of Diastolic Dysfunction in the ZSF-1 Rat Model of Pulmonary Hypertension Due to Left Heart Disease
08:57

Pulsed Wave Doppler Assessment of Diastolic Dysfunction in the ZSF-1 Rat Model of Pulmonary Hypertension Due to Left Heart Disease

Published on: May 22, 2026

Agentic Autodiscovery of Diastolic Dysfunction Phenotypes from Surface Electrocardiogram.

Ankush D Jamthikar, Aditya Shanmugham, Sajeev Singh

    Medrxiv : the Preprint Server for Health Sciences
    |July 3, 2026
    PubMed
    Summary

    Artificial intelligence (AI) now assesses left ventricular diastolic dysfunction (LVDD) using ECGs, synthesizing data to improve heart failure predictions. This automated approach enhances diagnostic accessibility beyond traditional echocardiography.

    More Related Videos

    Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism
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    Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

    Published on: September 1, 2014

    Related Experiment Videos

    Last Updated: Jul 4, 2026

    Pulsed Wave Doppler Assessment of Diastolic Dysfunction in the ZSF-1 Rat Model of Pulmonary Hypertension Due to Left Heart Disease
    08:57

    Pulsed Wave Doppler Assessment of Diastolic Dysfunction in the ZSF-1 Rat Model of Pulmonary Hypertension Due to Left Heart Disease

    Published on: May 22, 2026

    Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism
    11:04

    Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

    Published on: September 1, 2014

    Area of Science:

    • Cardiology
    • Artificial Intelligence
    • Medical Diagnostics

    Background:

    • Left ventricular diastolic dysfunction (LVDD) is a key factor in heart failure (HF).
    • Current LVDD assessment via echocardiography lacks scalability.
    • Generative AI can synthesize tissue Doppler imaging (TDI) from ECGs, addressing scalability limitations.

    Purpose of the Study:

    • To evaluate agentic AI for LVDD assessment using raw ECG or synthetic TDI waveforms.
    • To explore automated model-discovery frameworks for complex AI architectures.

    Main Methods:

    • Developed two attention-based agentic AI architectures using an LLM-driven refinement framework.
    • Optimized transfer-learning and multimodal architectures via autonomous proposal, validation, and selection.
    • Validated models in paired ECG-echocardiography studies and large external cohorts for HF mortality and structural heart disease.

    Main Results:

    • ECG-based AI achieved AUCs of 0.87 and 0.83 for LVDD classification.
    • Synthetic TDI-based AI achieved AUCs of 0.82 and 0.80.
    • Both models significantly stratified HF mortality risk and showed associations with structural heart disease.

    Conclusions:

    • Agentic auto-discovery enables efficient, data-driven LVDD assessment from ECGs.
    • The AI approach combines transfer learning and architecture optimization for generalizability.
    • This method may broaden access to diastolic function assessment, complementing echocardiography.