Join us on Friday 16th of May for a webinar titled “Federated Learning for Neurodegenerative Disease Research: A New Path to Risk Reduction and Better Care”. This is a joint webinar involving LETHE, BRAINTEASER and HEREDITARY projects.

It will begin providing the audience with an introduction to federated learning and then dive into examples of how federated learning is being used in the three projects. There will be a chance at the end of the webinar for the audience to participate and ask our panellists questions. Don’t miss out, sign up now!

Agenda:
– Welcome and Introduction – Natalia Allegretti, Senior Innovation Projects Manager (ECHAlliance) – 5 mins
– Introduction to Federated Learning and the Lethe Example – Hannes Hilberger, Researcher (FH JOANNEUM) – 15 minutes
– Bridging Projects: How Federated Learning in HEREDITARY project can advance Brainteaser AI models – Umberto Manera, Assistant Professor (University of Turin) – 15 minutes
– Questions from the audience – 25 minutes

Meet the speakers

Natalia Allegretti

Senior Innovation Projects Manager

ECHAlliance

Hannes Hilberger

Researcher

FH JOANNEUM

Umberto Manera

Assistant Professor

University of Turin

Learn more about the EU projects

LETHE is leading to a more personalized risk factor prevention for persons in the initial stages of cognitive decline, thereby empowering people to an active and healthy lifestyle. Expansion of  digital-enabled health preventive approaches, by reaching out to large populations, can save healthcare systems costs on expensive traditional interventions and confer benefits for the wider society.

BRAINTEASER is a data science project that seeks to exploit the value of big data, including those related to health, lifestyle habits, and environment, to support patients with amyotrophic lateral sclerosis and multiple sclerosis and their clinicians. Taking advantage of cost-efficient sensors and apps, BRAINTEASER will integrate large, clinical datasets that host both patient-generated and environmental data.

HEREDITARY aims to improve disease detection, treatment response preparation, and medical knowledge exploration through an integrated framework that incorporates diverse health data, including genetics, while ensuring privacy compliance.