4 minute read
DI Europe Summer 23
Mélisande Rouger
Brain Health Takes Center Stage at EAN 2023
Nearly 8,000 delegates gathered both onsite and online for EAN 2023, the 9th annual meeting of the European Academy of Neurology (EAN) that unfolded on July 1 – 4 in Budapest, Hungary.
EAN is a young society ready to embark the future, according to EAN president Prof. Paul Boon, who delivered his Welcome Address in the Opening session on the first day of the conference.
“We are a growing community, with more than 45,000 members from more than 47 countries,” he told a packed audience in the Main Auditorium of Hungexpo, which hosted the meeting this year.
With an all time high of submitted (2,318) and accepted abstracts (1,912), EAN 2023 delivered the latest research in neurological disorders including stroke, dementia, movement and sleep disorders, and rare neurological diseases.
Brain health demands more cooperation
Brain health was a special focus of the meeting this year and neurologists expressed concern about the steeply rising prevalence of brain diseases worldwide.
“At least one in three people will have a neurological disorder, but new data not yet published indicate that this number may be higher, actually 40 percent,” Boon said. “And to make things worst, it’s increasing.”
Neurological disorders are the single most important contributor to disability-adjusted life year (DALYs) and early mortality. “The cost of all cancers added to the cost of all cardiovascular diseases together is inferior to the cost of neurological disorders,” he said. “It’s a tremendous burden to society.”
This burden demands a holistic approach that goes far beyond diagnosis and treatment. “Most of the people with brain conditions may not have access to diagnosis, he explained.
The situation demands urgent action from all stakeholders, echoed Prof. Claudio Bassetti, EAN Past President and Vice President of the European Brain Council. “It’s time we work together across different disciplines of neuroscience to try to maximize the ways to diagnose, rehabilitate and also prevent diseases,” he said.
The EAN has produced the “One Brain, One Life, One Approach” white paper, detailing its approach for brain health in Europe, including strategic pillars such as awareness, education, intersectoral approach, and research. The EAN also launched a major initiative in its headquarters in Vienna to bring all stakeholders beyond medical associations and companies, and also organizes the Brain Health Summit. “We are in a network where different health providers need to work together across the countries, and also across the continent,” Bassetti said.
Huge amount of data and AI
Another overarching theme of the congress was big data, and experts
discussed challenges and opportunities of the rising tide of data neurologists now have to deal with. “The amount of data available in neurology, neuroscience, neurobiology and related disciplines is rising exponentially and challenging clinicians’ ability to analyze and use those data,” said Prof. Maria J. Molnar from Budapest, Hungary, who co-chaired a dedicated session with Prof. Charlotte Cordonnier from Lille, France.
“Data available range from routinely collected clinical data and population health data, through genomics and other omics and to clinical diagnostics – i.e. MRI and neurophysiology,” she added.
Cordonnier, a professor of neurology and head of the department of neurology and stroke center at the Lille University Hospital, focused on multi-modal data approaches to predict clinical outcomes after stroke. “We have to combine different types of data – from medical
imaging, demographics, biology medical imaging data and clinical assessment – and observe how they interact with each other to predict clinical outcomes after stroke,” she said.
Clinicians already combine different data every day when they assess outcome after stroke, for example with the ICH score. But there are limitations to human decision making capacities, Cordonnier insisted. “It is challenging to be able to integrate and generate predicting modeling, due to data heterogeneity and the different semantics of all these variables,” she said.
Machine learning-fed algorithms could enhance the ability to capture a broader range of stroke-related factors and their interactions, leading to more robust predictive models, she concluded.