There are over 50 million people worldwide living with dementia and by 2030 this number is expected to reach 82 million. The global health issue is exacerbated by the fact that the diagnosis of dementia is currently a slow process that can take numerous years and involve several clinic visits.
Diagnosis is often at a late stage in the progression of the condition. Consequently, intervention is concerned mostly with management of dementia rather than treatment. This results in an economic and social burden which could be significantly reduced by early diagnosis and delayed progression. To achieve this requires new methods and tools for prediction and earlier diagnosis.
Intelligent digital tools for dementia risk estimation
AI-Mind aims to shorten the time to diagnosis significantly and allow for diagnosis at the mild cognitive impairment (MCI) stage, when there are no severe structural cerebral defects and intervention is still possible. It will extend the ‘dementia-free’ period among MCI patients by offering diagnosis and early intervention.
The project involves developing two artificial intelligence based digital tools that will be integrated into a cloud-based diagnostic support platform. These tools will analyse existing and routinely collected data in an innovative manner: AI-Mind Connector automates identification of early brain network disturbances from electroencephalography; AI-Mind Predictor then enriches the data with genetic and cognitive information to provide an early marker of dementia risk.
The EUR 14 million project is led by Oslo University Hospital and has 15 partners across eight European countries. The project, which kicked-off in March 2021, is funded for 5 years and has nine integrated work packages culminating in the delivery of a cloud-based platform containing the two AI tools.
To realize these aims, it is vital that health data from participants is processed in a secure and compliant way and is harmonized and governed across the various sites. DNV’s involvement includes, delivering a guideline for legal and ethical data processing; developing a framework for data governance and data management framework; designing and implementing a data model; and developing and implementing methods for continuous data quality assurance.