On 17 January, the third event in the LETHE webinar series, titled “Beyond the Hype: AI in Dementia – From Early Risk Detection to Disease Treatment,” focused on how Artificial Intelligence (AI) is being applied to improve prevention, diagnosis, and treatment. The webinar, moderated by Cindy Birck from Alzheimer Europe attracted over 70 participants and featured presentations from four EU-funded projects: AI-Mind, PROMINENT, ADIS, and LETHE.
Ainar Drews from the University of Oslo presented work from the AI-Mind project, which focuses on developing AI tools to detect early signs of dementia in individuals with mild cognitive impairment (MCI). The project aims to create two AI-driven tools: one to uncover changes in brain connectivity and another to predict whether a person with MCI is likely to progress to dementia. These tools are designed to support both researchers and clinicians in making better-informed decisions and providing personalised care.
Antti Tolonen from Combinostics shared work from the PROMINENT project, which is developing a digital platform to support diagnosis and treatment planning in neurodegeneration. The platform that will be developed is aimed at integrating diagnostic algorithms, imaging biomarkers and an interface for specialists working in memory clinics. The project also focuses on the harmonisation of data collected from multiple European cohorts to ensure the development of a robust model and to validate it in future.
Sophia Krix from Fraunhofer SCAI highlighted work from the ADIS project, which explores digital biomarkers for Alzheimer’s disease, specifically focussing on sleep disturbances and immune system profiling. She also presented some preliminary findings through the analysis of data from wearable devices, highlighting that sleep behaviour and movement patterns could potentially help identify individuals at risk of developing dementia.
Markus Bödenler from FH Joanneum presented on the LETHE project, which is developing predictive models and digital tools to support lifestyle interventions for dementia prevention. The project uses data from clinical trials and memory clinics to assess an individual’s risk of cognitive decline. Its AI framework aims to allow clinicians to identify modifiable risk factors, such as diet and physical activity and tailor interventions to individual needs.
The panel discussion explored challenges such as the need for transparency, inclusivity in data collection, and overcoming barriers to adoption among clinicians. The importance of ethical considerations and collaboration across disciplines was emphasised throughout the session.
The webinar was recorded and can be viewed below.
