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Deep Learning-based 12-Lead Electrocardiogram for Low Left Ventricular Ejection Fraction Detection in Patients.

Yuxin Hou1, Zhiping Fan2, Jiaqi Li1

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China; Centre for Collaborative Research, Shanghai University of Medicine and Health Sciences, Shanghai, China.

The Canadian Journal of Cardiology
|September 29, 2024
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Summary
This summary is machine-generated.

An artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm accurately identifies patients with reduced left ventricular ejection fraction (LVEF), a key indicator of heart failure. This AI-ECG tool offers efficient, prompt, and cost-effective early screening for heart failure.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Reduced left ventricular ejection fraction (LVEF) is a primary indicator of heart failure, necessitating early detection for effective management and mortality reduction.
  • Prompt identification of low ejection fraction is critical for timely intervention and improved patient outcomes in heart failure management.

Purpose of the Study:

  • To develop and validate an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm for identifying patients with low ejection fraction.
  • To predict left ventricular ejection fraction (LVEF) values using AI-ECG technology.
  • To assess the algorithm's accuracy and efficiency as a screening tool for early heart failure detection.

Main Methods:

  • Utilized electrocardiogram (ECG) data as input for an AI algorithm to predict low ejection fraction probability and estimate LVEF values.
  • Conducted a 5-year follow-up study on individuals with initially normal LVEF.
  • Performed external validation using the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database to assess algorithm performance.

Main Results:

  • The AI-ECG algorithm achieved an area under the curve (AUC) of 0.965 for detecting LVEF ≤ 50% on the test set, with 92.8% accuracy, 88.8% sensitivity, and 92.9% specificity.
  • For LVEF regression, the algorithm demonstrated a mean absolute error of 5.28 on the testing set and 9.56 during external validation (AUC 0.848).
  • False positive results were associated with a significantly higher likelihood of developing low ejection fraction (26.2% vs 2.0%; P < 0.0001).

Conclusions:

  • The AI-ECG algorithm accurately identifies low ejection fraction, proving effective for early heart failure detection.
  • The AI-ECG algorithm serves as an efficient, prompt, and cost-effective screening tool for identifying individuals at risk of heart failure.
  • This technology holds significant potential for improving early heart failure diagnosis and management.