Simula: Arrhythmic mitral valve prolapse
The Simula research group focuses on improving the understanding and risk assessment of arrhythmic mitral valve prolapse by combining artificial intelligence (AI), computational modeling, and advanced imaging analysis.
Using AI-driven segmentation and machine learning techniques, we automate the extraction of mitral valve features from cardiac magnetic resonance (CMR) imaging, enhancing precision and reproducibility. We integrate patient-specific biomechanical and electrophysiological simulations to study the interplay between mitral valve morphology and arrhythmogenic risk. By developing data-driven models and quantitative imaging biomarkers, we aim to refine patient-specific diagnostics and improve risk stratification.
Publications
- Stretch of the papillary insertion triggers reentrant arrhythmia: an in silico patient study (Link to publication)
- Submitted conference paper: Arrhythmic mitral valve syndrome: insights from left ventricular end-systolic shape analysis
Contributions
- Computing in Cardiology 2024: Giulia Monopoli, Talk about “DeepValve: the first automatic detection pipeline for the mitral valve in Cardiac Magnetic Resonance imaging”
- MMIV conference 2024, Giulia Monopoli, 3rd prize for best poster presentation.
Researchers involved
Last updated 2/20/2025