Project Description

 

BRAINTEASER

Full project name: “Bringing Artificial Intelligence home for a better care of amyotrophic lateral sclerosis and multiple sclerosis”

Small description: BRAINTEASER aims to integrate societal, environmental and human health data to develop patient stratification and disease progression models for Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS). The data collected will be integrated in comprehensive Artificial Intelligence (AI) models able to address the need of preventive medicine and early risk prediction of disease fast progression and adverse events.

ALS and MS are two very complex degenerative neurological diseases but with very different clinical picture, evolution, prognosis and therapies. Common features are that both diseases affect the nervous system, they are chronic, progressive and modify significantly the quality of life of the patients and their families.

The project 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.

Programme + Grant Agreement: H2020 GA n. 101017598

Duration: 01/01/2021 – 01/12/2024 (48 months)

Scope and objectives: BRAINTEASER will develop a system able to guarantee cybersecurity and data ownership to the patients. It 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. Outcomes from the project will also provide a coherent and integrated set of recommendation for public health authorities to support the transition of the current approach to health from reactive to predictive, paving the path toward a healthier and more fulfilling life as long as possible.

BRAINTEASER main goals can be summarized as follow:

  • To investigate and model ALS and MS progression for patients, who demand to plan their future and being assisted in their daily needs, and for clinicians, who need to deepen disease understanding to personalise patients’ treatment and prevent adverse events and fast disease progression,
  • To enforce the advantage of using AI models in concert with current clinical approaches by introducing innovative descriptors of clinical outcomes, integrating and managing multidimensional datasets, stratifying patients and characterizing the disease evolution to design personalized health and care pathways,
  • To enforce the use of AI models in hospital, home-care and in research adopting an open science paradigm that makes scientific research results accessible to all levels of society, at the same time respecting the privacy and patients’ data ownership, and actively involving end users in the technological solution co-design, implementation and commercialization to make sure the project’s results will soundly respond to real needs.

Consortium: Universidad Politécnica de Madrid (SP), Università degli Studi di Padova (IT), FCIÊNCIAS.ID Associação para a Investigação e Desenvolvimento de Ciências (PT), Università degli Studi di Torino (IT), Instituto De Medicina Molecular – João Lobo Antunes (PT), Servicio Madrileño de Salud (SP), Fondazione Istituto Neurologico Nazionale Casimiro Mondino (IT), Preduzeće za informacione tehnologije i elektronskotrgovanje Belit d.o.o. (RS), InSilicoTrials Technologies S.p.A (IT), ECHAlliance Company Limited by Guarantee (IE), The European Brain Council AISBL (BE).

Website: brainteaser.health and on CORDIS

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