
Doctor Panteleimon Pantelidis
National & Kapodistrian University of Athens, Athens (Greece)
Membership:
ESC Professional Member
EHRA Member
Biography
Panteleimon Pantelidis is a PhD researcher at the NKUA, Greece, while clinically based at “Sotiria” Hospital in Athens. He specializes 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 60 scientific publications, including 40 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
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