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

Project description

A smart way to predict neurological disease progression

AI systems can be used to develop models able to predict the progression of multiple sclerosis and amyotrophic lateral sclerosis. While both are very complex, chronic and progressive degenerative neurological diseases, their clinical evolution, prognosis and therapies are different. In this context, the EU-funded BRAINTEASER project will develop a system of wearable sensors to enable prediction and advance clinical decision-making and prevention. Specifically, software and apps will be designed to embrace an agile and user-centred design approach, accounting for the technical, medical, psychological and societal needs of the specific users. One main aim is to assist clinicians in suggesting interventions that can delay the progression of the disease.

Objective

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.

Call for proposal

H2020-SC1-DTH-2018-2020

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Sub call

H2020-SC1-DTH-2020-1

Coordinator

UNIVERSIDAD POLITECNICA DE MADRID
Net EU contribution
€ 680 000,00
Address
CALLE RAMIRO DE MAEZTU 7 EDIFICIO RECTORADO
28040 Madrid
Spain

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Region
Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Higher or Secondary Education Establishments
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Total cost
€ 680 000,00

Participants (13)