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Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

Cardiomyopathy III: Hypertrophic Cardiomyopathy

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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...
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Cardiomyopathy I: Introduction and Classification01:25

Cardiomyopathy I: Introduction and Classification

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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...
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Hypertension III: Clinical Manifestations and Diagnostic Studies01:30

Hypertension III: Clinical Manifestations and Diagnostic Studies

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Hypertension is asymptomatic and also referred to as the "silent killer" until it progresses to a severe stage or causes target organ disease. Patients may experience symptoms stemming from the strain on blood vessels and tissues in various organs or the heart's increased workload.Physical exams might show no abnormalities other than high blood pressure. Signs of vascular damage, when present, correspond to the organs supplied by the affected vessels, leading to target organ damage. For...
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Electrocardiogram01:29

Electrocardiogram

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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...
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Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

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Heart failure (HF) is a progressive syndrome involving ventricles that leads to inadequate cardiac output. It can be classified based on location and output or ejection fraction. Ejection fraction (EF) is an essential measurement in the diagnosis and surveillance of HF. Reduced EF corresponds to systolic heart failure (HFrEF). However, HF with preserved ejection fraction (HFpEF) is becoming increasingly prevalent. Also known as diastolic HF, this form of HF is related to aging. The...
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In Silico Clinical Trials for Cardiovascular Disease
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Artificial intelligence-enabled classification of hypertrophic heart diseases using electrocardiograms.

Julian S Haimovich1,2,3, Nate Diamant4, Shaan Khurshid2,3,5

  • 1Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.

Cardiovascular Digital Health Journal
|April 27, 2023
PubMed
Summary

Artificial intelligence (AI) effectively detects and classifies left ventricular hypertrophy (LVH) using electrocardiograms (ECGs). This AI model surpasses traditional clinical rules for diagnosing LVH, improving cardiac care.

Keywords:
Artificial intelligenceCardiac amyloidosisElectrocardiographyHypertrophic cardiomyopathyHypertrophic heart disease

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Differentiating cardiac diseases associated with left ventricular hypertrophy (LVH) is crucial for accurate diagnosis and patient management.
  • Current diagnostic methods for LVH can be complex and require expert interpretation.

Purpose of the Study:

  • To evaluate the efficacy of an artificial intelligence-enabled analysis of the 12-lead electrocardiogram (ECG) for automated detection and classification of LVH.
  • To assess if AI can differentiate various causes of LVH.

Main Methods:

  • A convolutional neural network (CNN) was trained on 12-lead ECG data from over 50,000 patients with LVH.
  • The AI model, termed 'LVH-Net', regressed LVH etiologies using numerical ECG representations, age, and sex.
  • Single-lead ECG models were developed and compared against traditional ECG measures and clinical rules.

Main Results:

  • LVH-Net demonstrated high accuracy in classifying LVH etiologies, with AUCs ranging from 0.69 to 0.95.
  • Specific AUCs included cardiac amyloidosis (0.95), hypertrophic cardiomyopathy (0.92), and aortic stenosis (0.90).
  • Single-lead AI models also showed good performance in discriminating LVH causes.

Conclusions:

  • AI-enabled ECG analysis offers a promising approach for the detection and classification of LVH.
  • The developed AI model outperforms existing clinical ECG-based rules for LVH diagnosis.