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Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

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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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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...
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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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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...
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Diagnostic Accuracy of Machine Learning Algorithms in Electrocardiogram-Based Heart Failure Detection: A Systematic

Mustafa Eray Kilic1, Mehmet Emin Arayici2, Oguz Akbilgic3

  • 1Department of Cardiology, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye.

The Canadian Journal of Cardiology
|December 19, 2025
PubMed
Summary

Artificial intelligence (AI) applied to electrocardiograms (ECGs) shows promise for detecting heart failure (HF). Performance varies based on how heart failure is defined and ejection fraction thresholds, necessitating standardized endpoints for reliable AI-ECG implementation.

Keywords:
Electrocardiographyartificial intelligencediagnostic accuracyheart failureleft ventricular systolic dysfunctionmeta-analysis

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Artificial intelligence (AI) applied to electrocardiograms (ECGs) shows potential for heart failure (HF) detection.
  • Existing studies report heterogeneous performance, often conflating left ventricular systolic dysfunction (LVSD) with the clinical syndrome of HF.

Purpose of the Study:

  • To systematically review and meta-analyze the diagnostic accuracy of AI-ECG for detecting HF and LVSD.
  • To investigate factors influencing AI-ECG performance, including target definition and ejection fraction (EF) thresholds.

Main Methods:

  • Systematic review and meta-analysis of 40 unique patient cohorts following PRISMA-DTA guidelines.
  • Hierarchical bivariate modeling to synthesize diagnostic accuracy, with stratification and multi-threshold analysis for EF heterogeneity.
  • Meta-regression analysis to examine covariates like external validation, lead configuration, and AI model architecture.

Main Results:

  • The primary analysis showed pooled sensitivity of 85.9% and specificity of 80.9% (HSROC AUC = 0.902).
  • Performance varied significantly based on the definition of the target condition (LVSD vs. clinical HF) and EF thresholds.
  • 12-lead ECGs and convolutional neural network architectures were associated with higher specificity (p=0.003 and p=0.024, respectively).
  • A secondary analysis of HF classification models yielded pooled sensitivity of 96.2% and specificity of 92.1%.

Conclusions:

  • AI-ECG demonstrates significant but variable diagnostic performance for HF and LVSD.
  • Accurate implementation requires careful consideration of target condition definition, EF thresholds, and methodological factors.
  • Standardized endpoints are crucial for reliable and reproducible AI-ECG applications in clinical practice.