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Deep learning algorithm for predicting left ventricular systolic dysfunction in atrial fibrillation with rapid

Joo Hee Jeong1, Sora Kang2, Hak Seung Lee2

  • 1Division of Cardiology, Department of Internal Medicine, Korea University College of Medicine, Korea University Anam Hospital, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea.

European Heart Journal. Digital Health
|November 20, 2024
PubMed
Summary

Artificial intelligence accurately predicts left ventricular systolic dysfunction in atrial fibrillation patients with rapid ventricular response. This AI tool aids in early diagnosis and treatment selection in outpatient settings.

Keywords:
Artificial intelligenceAtrial fibrillationDeep learningLeft ventricular ejection fractionRate control

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Left ventricular ejection fraction (LVEF) is critical for managing atrial fibrillation (AF) with rapid ventricular response (RVR).
  • Real-time LVEF assessment is challenging in outpatient cardiology settings.
  • LV systolic dysfunction (LVSD) impacts treatment strategies for AF patients.

Purpose of the Study:

  • To validate an AI-based deep learning algorithm for predicting LVSD in AF patients with RVR.
  • To assess the algorithm's performance using 12-lead and 1-lead ECG.
  • To compare AI prediction with established biomarkers like NT-proBNP.

Main Methods:

  • External validation of a deep learning algorithm (residual neural network).
  • Analysis of a prospective cohort of 423 AF patients with RVR (2018-2023).
  • Primary outcome: LVSD detection (LVEF ≤ 40%) via 12-lead ECG; secondary outcome: 1-lead ECG prediction.

Main Results:

  • The AI algorithm showed fair performance in predicting LVSD (AUC 0.78) with a negative predictive value of 0.88.
  • AI performance was comparable to NT-proBNP (AUC 0.78 vs. 0.70).
  • 1-lead ECG prediction was less accurate (AUC 0.68) but maintained a high NPV (0.88).

Conclusions:

  • AI-based algorithms demonstrate competent performance in predicting LVSD in AF with RVR.
  • AI facilitates outpatient LVSD prediction, potentially enabling earlier therapeutic interventions.
  • This technology can improve symptom management for AF patients experiencing RVR.