Trustroke, an European artificial intelligence project aimed to optimise stroke treatment

1.5M Strokes by 2025

460K Deaths/year

25% ends up disabled

Only 40% Adherence

Trustroke, an European artificial intelligence project aimed to optimise stroke treatment

1.5M Strokes by 2025

460K Deaths/year

25% ends up disabled

Only 40% Adherence

Assists in managing stroke patients and assessing disease progression using clinical data, focusing on:

Clinical worsening leading to unplanned hospital readmissions.

Poor mobility, incomplete recovery and unfavorable clinical long-term outcomes.

Stroke recurrence.

Assists in managing stroke patients
and assessing disease progression using
clinical data, focusing on:

Clinical worsening leading to unplanned hospital readmissions.

Poor mobility, incomplete recovery and unfavorable clinical long-term outcomes.

Stroke recurrence.

Main Objectives

Federated Learning network to enable secure AI training

Trustworthy and validated AI solution for stroke risk assessment

Framework for patient empowerment and communication

A flexible and scalable platform clinically validated

Proof of concept study to improve stroke patient pathway

Federated Learning network to enable secure AI training

Trustworthy and validated AI solution for stroke risk assessment

Framework for patient empowerment and communication

A flexible and scalable platform clinically validated

Proof of concept study to improve stroke patient pathway

An ischemic stroke happens when the blood supply to a part of the brain is cut off. This is caused by the blockage of blood flow in a vessel that supplies brain.

The faster blood flow is restored to the brain, the better chance someone has of making a good recovery.

NIHSS ((National Institute of Health Stroke Scale) score is determined on admission and at discharge. It has a predictive value related to functional outcome at 90 days.

Early supported discharge is not common clinical practice and there is a lack of clear
criteria to select suitable patients.

Modified Rankin Score (mRS) is a disability scale mainly based on clinical reported
outcomes.

Existance of some acceptable models for short-term risk prediction but not widely used
due to the amount of inputs required.

Lack of high-quality, harmonised
and transparent datasets.

Lack of trustworthiness of predictive models: Current predictive models are not validated or clinically implemeted due to lack of robustness.

Lack of effective human-centred solutions.

Federated artificial intelligence model network trained by European hospitals to be used in the platform.

Stroke management platform implemented in the stroke units from hospitals in Spain, Italy, Belgium and Slovenia and validated over 2100 outpatients and their caregivers.

Evidence-based recommentadations for the use of trustworthy risk predictors to gain adherence to treatment.

Robust AI-based scoring system for five crucial end points in the management of stroke, with associated

Improvement in prevention
and case management

Reducing by 10-20% the risk of stroke recurrence, mortality and hospital readmissions

Novel quantitative metrics in clinical
practice, after regulatory approval

Revised international clinical guidelines

Treatment adherence increase up to 80%

In 2030, over 1 milion acute ischemic stroke patients and 9 milions stroke survivors could benefit from the Trustroke tool.

Publications

Publications

Partners

Partners

Stroke Organizations & Clinical Needs and Testing

Federated Learning Network Development

AI Algorithms Development

Sustainability, Legal & Ethical Regulations

Stroke Associations Coalition

Patient Communication & Monitoring

Data FAIRification & Trustworthiness

User Experience Design

Stroke Organizations & Clinical Needs and Testing

Federated Learning Network Development

AI Algorithms Development

Sustainability, Legal & Ethical Regulations

Stroke Associations Coalition

Patient Communication & Monitoring

Data FAIRification & Trustworthiness

User Experience Design

Project Coordination Team

Carlos Molina

Clinical Lead Head of Stroke Unit

Hospital Universitari Vall d'Hebron / VHIR

carlosav.molina@vallhebron.cat

Estela Sanjuan

Horizon Europe Project Coordinator

Hospital Universitari Vall d'Hebron / VHIR

estela.sanjuan@vhir.org

Project Coordination Team

Carlos Molina

Clinical Lead
Head of Stroke Unit

Hospital Universitari Vall d'Hebron / VHIR
carlosav.molina@vallhebron.cat

Estela Sanjuan

Horizon Europe
Project Coordinator

Hospital Universitari Vall d'Hebron / VHIR
estela.sanjuan@vhir.org

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