Virtual Human Twins (VHTs) for integrated clinical decision support in prevention and diagnosis HORIZON-HLTH-2027-03-TOOL-04

Objectives (what we will deliver)

O1 — Patient-specific NeuroVHT models that are multi-scale (behaviour → signals → imaging) and longitudinal (updates with new evidence over time), producing an interpretable risk state and confidence.
O2 — Integrated multi-modal screening pipeline that fuses digital biomarkers (pen/keyboard/gait/voice), EEG, and multimodal neuroimaging (MRI+PET) into a unified VHT representation.
O3 — Clinically validated decision support for prevention/diagnosis: earlier identification of high-risk individuals, reduced false referrals, and support for personalised diagnostic workups.
O4 — Interoperable assets made available to the European VHT ecosystem (models, pipelines, documentation, evaluation protocols), aligned with platform specifications and community practices.

Approach

B1. Multi-modal data backbone. Combine: (i) neuroimaging (sMRI, FDG-PET), (ii) electrophysiology (resting-state EEG; visual stimulus EEG), (iii) digital biomarkers from at-home or low-cost tests: handwriting spirals, keystroke dynamics, gait accelerometers, and voice.
B2. Representation-first modelling. Build on our demonstrated advantage of transforming raw signals into information-rich representations (e.g., time–frequency images) and then applying deep models/transfer learning for robust screening.
B3. Hybrid VHT modelling.