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CORDIS

BRinging Artificial INTelligencE home for a better cAre of amyotrophic lateral sclerosis and multiple SclERosis

Description du projet

Une approche intelligente pour prédire la progression des maladies neurologiques

Les systèmes d’IA peuvent permettre de développer des modèles capables de prédire la progression de la sclérose en plaques et de la sclérose latérale amyotrophique. Bien que ces deux pathologies soient des maladies neurologiques dégénératives à la fois complexes, chroniques et progressives, leur évolution clinique, leur pronostic et leur traitement sont différents. Dans ce contexte, le projet BRAINTEASER, financé par l’UE, compte développer un système de capteurs portable permettant de prédire les pathologies et d’améliorer la prise de décision et la prévention clinique. Plus spécifiquement, l’initiative mettra au point des logiciels et des applications autour d’une approche de conception agile et centrée sur l’utilisateur, en tenant compte des besoins techniques, médicaux, psychologiques et sociétaux des utilisateurs spécifiques. L’un des principaux objectifs est d’aider les cliniciens à formuler des interventions susceptibles de retarder la progression de la maladie.

Objectif

Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) are chronic diseases characterized by progressive or alternate impairment of neurological functions (motor, sensory, visual, cognitive). Patients have to manage alternated periods in hospital with care at home, experiencing a constant uncertainty regarding the timing of the disease acute phases and facing a considerable psychological and economic burden that also involves their caregivers. Clinicians, on the other hand, need tools able to support them in all the phases of the patient treatment, suggest personalized therapeutic decisions, indicate urgently needed interventions.

Artificial Intelligence is the key to successfully satisfy these needs to: i) better describe disease mechanisms; ii) stratify patients according to their phenotype assessed all over the disease evolution; iii) predict disease progression in a probabilistic, time dependent fashion; iv) investigate the role of the environment; v) suggest interventions that can delay the progression of the disease.

BRAINTEASER will integrate large clinical datasets with novel personal and environmental data collected using low-cost sensors and apps. Software and mobile apps will be designed embracing an agile and user-centred design approach, accounting for the technical, medical, psychological and societal needs of the specific users.

BRAINTEASER will implement a system able to guarantee cybersecurity and data ownership to the patients; will provide quantitative evidence of benefits and effectiveness of using AI in health-care pathways implementing a proof-of-concept of its use in real clinical setting. Procedural requirements that support Software as Medical Device certification will be used involving clinicians and patients stakeholders and producing a set of recommendations for public health authorities. Results will be disseminated accordingly to an open science paradigm under the European Open Science Cloud initiative.

Appel à propositions

H2020-SC1-DTH-2018-2020

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Sous appel

H2020-SC1-DTH-2020-1

Coordinateur

UNIVERSIDAD POLITECNICA DE MADRID
Contribution nette de l'UE
€ 680 000,00
Adresse
CALLE RAMIRO DE MAEZTU 7 EDIFICIO RECTORADO
28040 Madrid
Espagne

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Région
Comunidad de Madrid Comunidad de Madrid Madrid
Type d’activité
Higher or Secondary Education Establishments
Liens
Coût total
€ 680 000,00

Participants (13)