T1.1
Aortic stenosis
The care of patients with aortic valve disease presents several challenges that require careful consideration. These challenges include determining the optimal timing for intervention, selecting the most appropriate valvular prostheses and best suited implantation techniques. These decisions must be tailored to each patient's unique anatomy, age and comorbidities.
To enhance the management of aortic valvular disease, ProCardio utilizes imaging datasets and leverages the power of artificial intelligence (AI) techniques. With this approach ProCardio aims to closely monitor the progression of aortic disease, determine the optimal timing for intervention, enhance the selection of appropriate patients for appropriate aortic valve replacement procedures (surgery vs TAVI), and accurately predict the outcome following surgical and transcatheter interventions.
Projects
- Automatic LVOT measurement and grading of aortic stenosis
- High frame rate imaging in aortic stenosis for tissue characterization of fibrosis (Fibroecho)
- Mechanical dispersion as a prognostic predictor in aortic stenosis (MediBlock TAVI)
- Prediction of progression and prognosis in aortic stenosis
Publiations
- Left Ventricular Mechanical Dispersion as a Predictor of need for Pacemaker implantation after TAVI (Link to publication)
- Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography (Link to publication).
- Early reverse remodeling by echocardiography after transcatheter aortic valve implantation (Link to publication).
- Non-invasive myocardial work in aortic stenosis: validation and improvement in left ventricular pressure estimation (Link to publication).
