Periodic Reporting for period 3 - BRAINTEASER (BRinging Artificial INTelligencE home for a better cAre of amyotrophic lateral sclerosis and multiple SclERosis)
Reporting period: 2023-07-01 to 2024-12-31
BRAINTEASER is a 48-month project that began on January 1st, 2021, co-funded by the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 101017598), under the thematic area of Health, Demographic Change, and Well-being. The project is carried out by a multidisciplinary consortium of 11 partners from 6 European countries.
The goal of BRAINTEASER is to integrate health, environmental, and societal data to develop advanced models for patient stratification and disease progression in Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS), two complex, degenerative neurological diseases with distinct clinical characteristics, progression patterns, prognoses, and treatment approaches. Despite their differences, both conditions profoundly impact the quality of life of patients and their families.
By combining large retrospective clinical datasets with novel patient-generated and environmental data, collected through low-cost sensors and mobile applications, BRAINTEASER enables the development of Artificial Intelligence (AI) tools to support precision medicine. These tools aim to facilitate early risk prediction of rapid disease progression and adverse clinical events.
All technical developments within BRAINTEASER follow an agile, user-centered design approach, carefully addressing the technical, clinical, psychological, and societal needs of the end users. The project provides quantitative evidence of the effectiveness and benefits of AI-driven solutions within real-world clinical pathways, demonstrating their potential through proof-of-concept deployments in both hospital and home-care settings. Additionally, the outcomes will inform a set of integrated recommendations for public health authorities, supporting a shift from reactive to predictive healthcare models.
Ultimately, BRAINTEASER strives to empower patients to lead healthier, more fulfilling lives for as long as possible.
The Key Objectives of BRAINTEASER are:
• To model and understand disease progression in ALS and MS, helping patients plan their futures and manage daily needs, while enabling clinicians to deliver personalized treatments and anticipate adverse events or rapid deterioration.
• To harness the power of AI to enhance current clinical practice by introducing innovative disease descriptors, managing multidimensional data, stratifying patients, and tailoring care pathways to individual needs.
• To promote the use of AI tools across clinical, home-care, and research environments, through an open science paradigm that ensures accessibility, protects data privacy and ownership, and actively involves end users in the co-design, deployment, and commercialization of technological solutions—ensuring they are truly aligned with real-world needs.
The goal of BRAINTEASER is to integrate health, environmental, and societal data to develop advanced models for patient stratification and disease progression in Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS), two complex, degenerative neurological diseases with distinct clinical characteristics, progression patterns, prognoses, and treatment approaches. Despite their differences, both conditions profoundly impact the quality of life of patients and their families.
By combining large retrospective clinical datasets with novel patient-generated and environmental data, collected through low-cost sensors and mobile applications, BRAINTEASER enables the development of Artificial Intelligence (AI) tools to support precision medicine. These tools aim to facilitate early risk prediction of rapid disease progression and adverse clinical events.
All technical developments within BRAINTEASER follow an agile, user-centered design approach, carefully addressing the technical, clinical, psychological, and societal needs of the end users. The project provides quantitative evidence of the effectiveness and benefits of AI-driven solutions within real-world clinical pathways, demonstrating their potential through proof-of-concept deployments in both hospital and home-care settings. Additionally, the outcomes will inform a set of integrated recommendations for public health authorities, supporting a shift from reactive to predictive healthcare models.
Ultimately, BRAINTEASER strives to empower patients to lead healthier, more fulfilling lives for as long as possible.
The Key Objectives of BRAINTEASER are:
• To model and understand disease progression in ALS and MS, helping patients plan their futures and manage daily needs, while enabling clinicians to deliver personalized treatments and anticipate adverse events or rapid deterioration.
• To harness the power of AI to enhance current clinical practice by introducing innovative disease descriptors, managing multidimensional data, stratifying patients, and tailoring care pathways to individual needs.
• To promote the use of AI tools across clinical, home-care, and research environments, through an open science paradigm that ensures accessibility, protects data privacy and ownership, and actively involves end users in the co-design, deployment, and commercialization of technological solutions—ensuring they are truly aligned with real-world needs.
Over the course of the project, significant progresses were made across technical, clinical, regulatory, and strategic domains, driving progress toward the overarching objectives. Initial efforts concentrated on gaining a comprehensive understanding of user needs and operational contexts, as well as gathering and analyzing retrospective clinical and environmental datasets. This foundational work allowed the development of an initial infrastructure for data collection and enabled the identification and design of AI models focused on disease progression and patient stratification for ALS and MS. Parallel to this, an open science approach was established, including the design and initial development of the BRAINTEASER ontology, tailored to model retrospective data for both conditions. A robust dissemination and communication strategy supported widespread awareness and engagement with the project’s aims and technologies.
Building on these foundations, the project moved toward real-world application and clinical integration. Detailed protocols and clinical guidelines were developed to test software tools and applications within hospital and home settings. These were accompanied by strategies for prospective data collection, aimed at supporting regulatory pathways for Software as a Medical Device (SaMD) certification. Technical development advanced across multiple layers (i.e. backend infrastructure, data flow management, and platform services) ensuring seamless provisioning to AI models and associated applications. This phase also saw the implementation and initial integration of AI models into clinical tools and the patient app, in alignment with defined interventions and evaluation criteria.
Regulatory documentation was rigorously reviewed to align with SaMD requirements, while AI models designed to monitor disease progression and stratify patients were validated and deployed. These tools enriched clinical decision-making by delivering real-time, personalized insights into the evolution of ALS and MS. The ontology also evolved to support new data and model integration. Throughout the project, open science principles were supported via the organization of Open Evaluation Challenges and the publication of datasets on the European Open Science Cloud (EOSC), fostering transparency and community engagement.
Stakeholder collaboration was further strengthened through the establishment and growth of the Community of Practice (COP), facilitating knowledge exchange and interdisciplinary cooperation. Dissemination activities have been continuously executed to promote the project’s technological, scientific, and practical achievements to a broader audience. Sustainability is being a key consideration, with tailored strategies ensuring that the developed tools, models, and infrastructures will remain viable and impactful beyond the project’s duration.
Building on these foundations, the project moved toward real-world application and clinical integration. Detailed protocols and clinical guidelines were developed to test software tools and applications within hospital and home settings. These were accompanied by strategies for prospective data collection, aimed at supporting regulatory pathways for Software as a Medical Device (SaMD) certification. Technical development advanced across multiple layers (i.e. backend infrastructure, data flow management, and platform services) ensuring seamless provisioning to AI models and associated applications. This phase also saw the implementation and initial integration of AI models into clinical tools and the patient app, in alignment with defined interventions and evaluation criteria.
Regulatory documentation was rigorously reviewed to align with SaMD requirements, while AI models designed to monitor disease progression and stratify patients were validated and deployed. These tools enriched clinical decision-making by delivering real-time, personalized insights into the evolution of ALS and MS. The ontology also evolved to support new data and model integration. Throughout the project, open science principles were supported via the organization of Open Evaluation Challenges and the publication of datasets on the European Open Science Cloud (EOSC), fostering transparency and community engagement.
Stakeholder collaboration was further strengthened through the establishment and growth of the Community of Practice (COP), facilitating knowledge exchange and interdisciplinary cooperation. Dissemination activities have been continuously executed to promote the project’s technological, scientific, and practical achievements to a broader audience. Sustainability is being a key consideration, with tailored strategies ensuring that the developed tools, models, and infrastructures will remain viable and impactful beyond the project’s duration.
BRAINTEASER will provide the MS and ALS patients, and those who take care of them the possibility to monitor the progress of the disease, and to have automatic feedback in case of need to contact the doctor outside the established routine, thus empowering patients to better manage their health providing digital health service (prognosis, prevention, assistance). The AI tools developed will allow a better prognosis thanks to: a) the integration of clinical data with genetic data, environmental data, sensor data and self-reported questionnaires; b) a better stratification of the patients based on the dynamics of phenotypic manifestations; c) a different perspective of the prediction tools, which allow a description over time and within a range of probabilities with respect to the progress of the disease. Moreover, BRAINTEASER will allow clinicians making decisions bearing in mind the global context of the patient's progress, and their general health, as well as their socio-economic situation, since all this information is integrated in the models. The open science approach will provide a single coherent, machine and human processable, searchable, re-usable and exploitable asset beyond the consortium itself and lasting also after the end of the project.