
Doctor Panteleimon Pantelidis
National & Kapodistrian University of Athens, Athens (Greece)
Membership:
ESC Professional Member
EHRA Member
Biography
Panteleimon Pantelidis is a senior cardiology trainee at “Sotiria” Hospital in Athens, and a PhD researcher at the NKUA, Greece, specializing in artificial neural networks for predicting arrhythmias. He holds an MSc in Data Science from the University of Stockholm and a medical degree from AUTh, Greece.
He has authored over 40 scientific publications, including 26 peer-reviewed journal papers and a book chapter. His presentations span major congresses like ESC & EHRA. Certified in various advanced medical- and biomedical data analysis-related courses, he is also proficient in English, German and conversational Swedish.
An ESC Professional Member, elected Nucleus Member of the ESC e-Cardiology WG (2024-26), and an EHRA Silver Member. Reviewer for several journals, with leadership roles in organizations like SSHMS. Awarded as an EHRA e-cardiology runner-up, a YIA finalist, and also received ESC educational grants, HSC scholarships and a WHBA award.
Enjoys sailing and playing guitar.
Contributor content
Presentation
Beyond black-box electrocardiogram analysis: saliency-guided deep learning for differential diagnosis of tachyarrhythmias
Presentation
ECG-XPLAIM: an eXPlainable Locally-adaptive Artificial Intelligence Model for arrhythmia detection from large-scale electrocardiogram data
Presentation
Coronary artery disease in adults with congenital heart disease: a systematic review and meta-analysis
Presentation
The hidden culprit: an arrhythmic mystery behind a case of sudden clinical deterioration
Presentation
Severe four-pulmonary vein stenosis following repeat catheter ablation for atrial fibrillation
Presentation
Generative adversarial networks (GANs) to produce synthetic 12-lead electrocardiogram signals for specific and rare diseases: a novel, powerful tool towards clinical advancements
Presentation
Inside the "brain" of an artificial neural network: an interpretable deep learning approach to paroxysmal atrial fibrillation diagnosis from electrocardiogram signals during sinus rhythm
Presentation

