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Cardiovascular disease touches nearly every stage of patient care. From the hospital to long-term monitoring at home. The e/MTIC cardiovascular research program brings together clinicians, engineers, and industry partners to define a shared research vision and roadmap, turning promising technologies into real improvements in patient care, faster.

Our strategy is to detect cardiovascular diseases earlier and treat them better and faster. We pave the way for technological healthcare products and solutions to be rapidly implemented in the healthcare system by combining our strengths, working towards a shared vision and win-wins for all partners involved.

Clinical Validation & Implementation
Extending focus and current research activities towards:

• Publications and methods in high-impact journals.
• First-in-human studies and follow-up trials.
• Development of ecosystems for RPM and AI integration into clinical workflows.

The 2026 roadmap builds on this foundation through four connected lines of technologies:

1. Sensor validation & remote monitoring: Validation of multimodal sensors: PPG, bio-impedance, speech, video. Wearable and continuous remote monitoring in clinical and home settings. Remote Patient Management (RPM) and telerehabilitation: impact on quality of life and functional capacity. Including advanced PDMS systems and decision support for hemodynamic and respiratory monitoring.
    
2. Imaging innovations: High-end and point-of-care imaging (multistaticultrasound, sPA, photoacoustic) for early detection and monitoring. Imaging-and AI-informed models for risk prediction and treatment planning. Preparation for valorization and start-up initiatives (e.g., AAA modeling).
    
3. Data & AI integration: Further development of data infrastructure and integration with the AI expertise center. Predictive models for arrhythmias (VT/VF, AF), heart failure readmissions, and vascular disease progression. Including alarm management and automated detection of clinical deterioration.
    
4. Modeling & digital twinning: Digital twins to forecast clinical course and intervention outcomes.