Area of research
Cardiology and Cardiovascular Medicine
Research interest
Research interests include Atrial Fibrillation Management and Outcomes, Cardiac Arrhythmias and Treatments, Cardiac electrophysiology and arrhythmias, and ECG Monitoring and Analysis.
State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology
Addressing contemporary threats in anonymised healthcare data using privacy engineering
Access to digital health technologies: personalized framework and global perspectives
Abstract 4370718: Transformer-based ECG beat foundation model reconstructs full 12-Lead morphology, vectorcardiogram and predicts peak heart rate in stress ECG
Abstract 4370694: Longitudinal Evaluation of Anti-Arrhythmic Drug Use to Predict Hospitalization or Death in Patients with Ventricular Tachycardia
Abstract 4371591: Automated End-to-End Framework for Extracting Raw ECG Waveforms and ST Segment Values from ECG Reports and Predicting ST Elevation by Machine Learning
Abstract 4370248: Novel Foundation Models for Detecting and Generating Text Reports of Atrial Fibrillation from 12-lead ECGs in a Large Registry
Abstract 4358583: Non-Contact Magnetocardiography Localizes Atrial Foci as Accurately as High-Resolution Contact ECG
Abstract 4370585: AI-based prediction of mortality in patients with ventricular tachycardia
Abstract 4369502: Identifying optimum ECG features to predict sudden cardiac arrest at varying time points before the event
Abstract 4366827: Large Language Models Detect Ventricular Tachycardia Recurrence in Clinical Notes and Enable Prediction of Clinical Outcomes at Scale
Mechanistic Insights From Trials of Atrial Fibrillation Ablation: Charting a Course for the Future
Spatially Conserved Spiral Wave Activity During Human Atrial Fibrillation
NOVEL INTERACTIONS BETWEEN STROKE RISK FACTORS IN THE YOUNG REVEALED BY INTERNET SEARCH ALGORITHMS APPLIED TO ELECTRONIC HEALTH RECORDS
IMPACT OF CARDIAC FIBER ORIENTATION ON ELECTRICAL DYSSYNCHRONY IN VENTRICULAR ECTOPY
Abstract 4137246: Atrial Fibrillation Wave Vectors Determined by Artificial-Intelligence Indicate Clinical Phenotypes
Abstract 4138724: Artificial-Intelligence Based Tracking of Atrial Fibrillation Waves that Exit Pulmonary Veins Predicts Response to Ablation
Abstract 4146553: Efficacy and Safety of Catheter Ablation in Patients with Hematologic Malignancies
The digital journey: 25 years of digital development in electrophysiology from an Europace perspective
A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease
Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies
Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography
Constructing bilayer and volumetric atrial models at scale
Abstract 16401: Optimizing ChatGPT to Detect VT Recurrence From Complex Medical Notes
Abstract 17420: Enhanced Identification of Cardiac Wall Motion Abnormalities: An Externally Validated Deep Neural Network Approach Outperforms Expert and Quantitative Analysis of Electrocardiograms
OBSTRUCTIVE SLEEP APNEA PORTENDS STROKE IN YOUNG INDIVIDUALS WITHOUT ATRIAL FIBRILLATION: A LARGE REGISTRY STUDY
VENTRICULAR TACHYCARDIA PREDICTS ATRIAL FIBRILLATION RECURRENCE POST ABLATION: A PROPENSITY SCORE-MATCHED ANALYSIS OF A LARGE PROSPECTIVE STUDY
Increases in dietary phosphate levels can augment atrial arrhythmias
Predicting success of atrial fibrillation ablation: comparing machine learning approaches of intracardiac electrograms
Abstract 17934: Performance of an Electrocardiographic Deep Learning Model for Detecting Wall Motion Abnormalities: An Analysis of Impact From Cardiovascular Comorbidities and Race in an External Validation Study