Full TitleBRinging Artificial INTelligencE home for a better cAre of amyotrophic lateral sclerosis and multiple SclERosis
BRAINTEASER aims to integrate societal, environmental and health data to develop patient stratification and disease progression models for Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS). 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 these chronic diseases affect the nervous system and progressively modify the quality of life of the patients and their families in a significant way.
BRAINTEASER will integrate large clinical datasets with novel patient-generated and environmental data collected using low-cost sensors and apps. The collected data will allow the development of Artificial Intelligence (AI) tools able to address the current needs of precision medicine, enabling early risk prediction of disease fast progression and adverse events. Technical solutions developed within the project will follow agile and user-centered approaches, accounting for the technical, medical, psychological and societal needs of the specific users.
The system developed in BRAINTEASER will provide quantitative evidence of benefits and effectiveness of using AI in health-care pathways, implementing a proof-of-concept of their use in real clinical setting. Outcomes from the project will also provide a coherent and integrated set of recommendations for public health authorities. BRAINTEASER will support the transition of current healthcare approaches from reactive to predictive, paving the path for patients toward a healthier and more fulfilling life as long as possible.
BRAINTEASER main goals can be summarized as follows:
- 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 to augment 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.
LASIGE’s team coordinated by Sara C. Madeira is responsible for the work package targeting patient stratification according to their phenotype assessed all over the disease evolution. It further actively participates in all others tasks concerning data science/artificial intelligent tasks. In particular, the development of advanced machine learning models to unravel disease mechanisms, predict disease progression, and suggest interventions that can delay disease progression, where patient stratification is key given patient heterogeneity both in ALS and MS.
Funding EntityEU H2020
ReferenceGrant Agreement. n.º 101017598
CoordinatorUniversidad Politecnica de Madrid (UPM)
PartnersUniversità degli studi di Padova (UNIPD), LASIGE/FCiências.ID, Universitá degli Studi di Torino (UNITO), Instituto Medicina Molecular - João Lobo Antunes (iMM), Servicio Madrileño de Salud (SERMAS), Fondazione Instituto Neurologico Nazionale Casimiro Mondino (MNDN-PV), Belit d.o.o., InSilicoTrials Technologies S.p.A (IST), ECHAlliance Company Limited by Guarantee, The European Brain Council AISBL (EBC)
Principal Investigator at LASIGESara C. Madeira
Team at LASIGE
- Andreia Martins
- Diogo Soares
- Eduardo Castanho
- Eleonora Auletta
- Helena Aidos
- Ruben Branco
- Sara C. Madeira