By prioritising patients’ quality of life and well-being, BRAINTEASER is transforming clinical care supporting the ability of public health systems to better predict, identify, and manage the risk factors that lead to amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS), as well as their complications and outcomes.
The project benefits professionals, patients, and caregivers by offering them tools to support creating a brighter personal, social, and economic future.
Patients and caregivers:
Gain more control over health decisions:
BRAINTEASER’s continuous monitoring system uses affordable sensors and app-based questionnaires, allowing patients and caregivers to track disease progression and take timely actions to manage their condition effectively.
ALS or MS patients can receive better predictions about their condition:
BRAINTEASER’s Artificial Intelligence tools analyse large datasets to better group patients, providing a clearer understanding of disease management, clinical needs, and personalised treatment effectiveness.
Health professionals and healthcare systems:
Benefit from improved resource planning in clinical practice and across the healthcare system:
BRAINTEASER provides doctors with a comprehensive view of patient progress, including health, socio-economic factors, and overall well-being, enabling more informed and efficient decision-making.
Research and scientific teams:
Profit from BRAINTEASER's efforts to shape a regulatory framework for digital tools and methods:
By introducing models for patient stratification and disease prediction, BRAINTEASER provides tools that can simulate clinical scenarios, support the design of clinical trials, aid in drug development, and enhance decision-making in healthcare, all while aligning with current regulatory guidelines.
Leverage cutting-edge technology and data insights through the European Open Science Cloud:
By organising open evaluation challenges, sharing experimental data, and publishing open-access research, BRAINTEASER accelerates the development of AI-driven solutions for ALS and MS, validating its methods and paving the way for advancements both within and beyond the project.
