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Bivariate meta-analysis of artificial intelligence for electrocardiographic atrial fibrillation detection: diagnostic accuracy, external validation, and clinical readiness of contemporary algorithms

30 August 2026 (15:15 - 16:00)
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Congress Presentation Part of: Atrial fibrillation detection: from signals to clinical application Diagnostic Methods ESC2026 Premium Access ESC Congress 2026 European Society of Cardiology

About the speaker

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Doctor Ali Rehman

Liaquat College of Medicine and Dentistry, Karachi (Pakistan)

7 More presentations in this session

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Risk stratification for atrial fibrillation in hypertrophic cardiomyopathy using a deep learning–based ECG model
Professor J. Choi (Seoul, KR)
Abstract
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A new multi-step diagnostic pathway using machine learning ECG analysis and wearables to detect Atrial fibrillation in embolic stroke of unknown source
Doctor F. Range (Duesseldorf, DE)
Abstract
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Continuous electrocardiographic monitoring improves atrial fibrillation detection after ischemic stroke
Doctor T. Chiuariu (Marseille, FR)
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Accuracy of atrial fibrillation labels in the MIMIC-IV ECG database: implications for artificial intelligence model development
Mr I. Valasakis (Glasgow, GB)
Abstract
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Correlation between left atrial echocardiographic parameters and electrocardiographic P-Wave indices in patients with paroxysmal atrial fibrillation
Professor W. Ammar (Cairo, EG)
Abstract

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Atrial fibrillation detection: from signals to clinical application

The Event

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ESC Congress 2026

30 August 2026 15:15 CET
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