معرفی
Oleg Aslanidi is a Professor of Biomedical Engineering whose research focuses on computational modeling of cardiac electrophysiology, particularly in the context of atrial fibrillation and digital twins for healthcare. His work integrates biophysical modeling, artificial intelligence, and medical imaging to develop personalized decision-support systems for clinical cardiology.
- Active in research with over 114 research outputs
- Primary Investigator on multiple BHF- and EPSRC-funded projects
- Editorial roles in journals such as Frontiers in Cardiac Electrophysiology
- Focus on translational modeling for ablation therapy and stroke risk prediction
His research interests lie at the intersection of engineering and medicine, with a strong emphasis on using computational tools to understand and treat cardiac arrhythmias. Key areas include atrial fibrillation mechanisms, thrombogenesis risk modeling, image-based patient-specific simulations, and AI-driven clinical predictions. He employs multi-scale modeling techniques that span from ion channels to whole-organ dynamics, often integrating MRI data for anatomical accuracy.
The recent trend in his publications shows a clear shift toward integrating artificial intelligence with biophysical models to enhance clinical decision-making. His work increasingly emphasizes interpretability in deep learning, non-Newtonian blood flow modeling, and personalized risk stratification for stroke and ablation outcomes. These articles span disciplines such as biomedical engineering, cardiology, computational fluid dynamics, and machine learning, reflecting a highly interdisciplinary approach.
His scientific contributions are recognized through active editorial and peer-review roles:
- Associate Editor, Frontiers in Cardiac Electrophysiology (2021–present)
- Peer reviewer for IEEE Access, Philosophical Transactions of the Royal Society A, and other leading journals
- Invited speaker at international workshops and conferences
As a research leader, Dr. Aslanidi has supervised several trainees and leads a research group focused on computational cardiology. He has secured significant grant funding, including from the British Heart Foundation and EPSRC, supporting projects such as:
- Computational decision support system for atrial fibrillation management (2024–2027)
- Image-based system for guiding ablation treatment (2018)
- Dissecting multifactorial mechanisms of atrial fibrillation (2015–2018)
- Evolution of arrhythmogenic substrate in canine atria (2012–2014)
His work contributes to UN Sustainable Development Goals related to good health and well-being, particularly through improving cardiovascular disease outcomes.
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