Back

Interpretable machine learning for predicting paroxysmal atrial fibrillation from automatically measured ECG features

21 November 2025 (08:00 - 18:00)
Organised by: Logo
Congress Presentation Part of: Poster session (1) Machine Learning Open Access ESC Digital & AI Summit 2025 European Society of Cardiology

About the speaker

Photo
Doctor Dimitrios Tachmatzidis

Aristotle University of Thessaloniki, Thessaloniki (Greece)

35 More presentations in this session

Presentation thumbnail
Development and validation of a parsimonious AI-based risk score for mortality in heart failure: a UK cohort study
Mr N. Ahmed (Oxford, GB)
Abstract Slides
Presentation thumbnail
Comparison of a novel scientific model of the memetic pattern based (MPA v3) and the ESC 2024 RF-CL algorithm for the detection of coronary artery disease: a retrospective multicentre study
Mr A. Buesser (CH)
Abstract Slides
Presentation thumbnail
Learning to scale: deriving data-driven scaling laws for ECG-optimized CNNs
Mr N. Jabareen (Berlin, DE)
Abstract Slides
Presentation thumbnail
Application of machine learning to predict timing of ventricular tachycardia onset
Mr A. Mann (Himachal Pradesh, IN)
Abstract Slides
Presentation thumbnail
Real-time detection of return of spontaneous circulation during cardiopulmonary resuscitation using AI and carotid Doppler ultrasound
Mr R. Mushunuri (Trondheim, NO)
Abstract Slides

Access the full session

Session thumbnail
Poster session (1)

The Event

Event poster

ESC Digital & AI Summit 2025

21 November - 22 November 2025
You may be interested in
thumbnail
Congress Presentation
Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study
thumbnail
Congress Session
Evolutions in arrythmia and syncope management
thumbnail
Congress Presentation
AI in cardiology: a force for progress? pro

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources