Brainteaser is a project funded by H2020 at European level, whose main objective is to use Artificial Intelligence (AI) systems to process data from patients diagnosed with Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS). The aim is to develop models able to predict the progression of the disease and thus monitor patients more efficiently and effectively.
More than 10 partners are involved in this project, which is coordinated by the Polytechnic University of Madrid and where, with great pride, we have the Faculty of Medicine of the University of Lisbon represented, through the Institute of Molecular Medicine.
We spoke with Professor Mamede de Carvalho, deputy director of the Faculty of Medicine at the University of Lisbon, neurologist at the Northern Lisbon University Hospital Centre, group leader at the Institute of Molecular Medicine (iMM-João Lobo Antunes) and Director of the Institute of Physiology, who kindly answered our questions about this BrainTEASER project. The research team led by Mamede Carvalho (Mamede Carvalho Lab) is one of the main partners and its experience in Amyotrophic Lateral Sclerosis is essential.
Tell us about the idea of this project and how you became partners?
The project was an initiative of other partners who invited us to participate given our data on Amyotrophic Lateral Sclerosis and our previous experience in interacting with other groups (in particular Professor Sara Madeira of the University of Sciences, LASIGE) in the analysis of "big-data" (data-mining) resorting to "machine learning" (artificial intelligence) processes. It is worth noting our collaborative relationship with another participating clinical group, at the University of Turin, which enabled this integration.
Can you tell us about what the project is about and what will be the role of your Laboratory in it?
The project is relatively complex, but in short it deals with data analysis of patients with Amyotrophic Lateral Sclerosis and Multiple Sclerosis (followed in an Italian centre) using machine learning instruments to find relationships between clinical, genetic and biomarkers, complemented by environmental data (such as pollution and sun exposure). To this are added several biological elements collected by sensors to be used by patients who agree to participate.
What long-term impact do you expect this project to have on amyotrophic lateral sclerosis and multiple sclerosis? What other projects can be derived from it?
In addition to a better understanding of diseases and the role of multiple biological and environmental elements in their expression and progression, the use of sensors with remote biometric data recording will allow understanding their role in the monitoring and treatment of neurological diseases (from the perspective of "precision medicine").
Artificial intelligence is one of the trend technologies in the health field. What advances do you think can be made in the area of neuroscience?
Artificial intelligence is not a very recent instrument, but it has benefited from computational and algorithmic development, in order to facilitate more precise and faster solutions. It can be used in the interpretation of data for diagnosis (as in the image), in continuous monitoring processes, in the execution of rapid interventions (such as, for example, in stopping a seizure crisis or an arrhythmia by the induction of electrical currents in the tissues) or interpreting data with self-learning algorithms. In Neurosciences, all areas can benefit, as long as the basic concept of highly competent clinicians is maintained, in the follow-up of patients. In the most immediate way, identifying solutions in complex situations may be of great interest.
What other projects does your Laboratory have in hand and what hopes do you have for the future in your area?
Our Institute has projects in several areas, such as neurophysiology, the interaction of dysautonomia-cardiac dysrhythmia (Professor Isabel Rocha), computational models of movement and behaviour (Professor Tiago Maia), in biological biomarkers of neurological diseases (amyotrophic lateral sclerosis, familial amyloid polyneuropathy and spinal muscular atrophy), including genetics and microRNA, in data analysis using machine-learning and the use of biosensors. I believe this is the future, and I am optimistic about our scientific competitiveness in upcoming times.
We thank Professor Mamede for his kindness in answering our questions and we wish this project a lot of success, as it will lead to better conditions for all patients suffering from ALS and MS.
For more details on this project, check the official website of the European Commission.
Sónia Teixeira
Editorial Team
