The number of people living with dementia worldwide is expected to rise to over 150 million by 2050. Yet, there are no existing treatments that can cure or halt the progression of dementia-causing diseases. This is partly the case because dementia diagnoses are typically only made once patients demonstrate noticeable symptoms. At this point, the diseases will likely have been spreading for over ten years and will have caused irreversible damage. For treatments to have the biggest chance of success, they need to be given at a much earlier stage.

The Early Detection of Neurodegenerative diseases (EDoN) initiative was established to address this unmet need. EDoN aims to develop a digital device that enables the detection of specific dementia-causing diseases 10-15 years before symptoms become noticeable. As part of EDoN, sleep patterns, physical activity levels, smartphone interactions, cognitive function and mood data will be collected in research participants using wearables and smartphone applications. Clinical measures, like brain scans, concomitantly collected will be used to train machine learning models to detect ‘fingerprint’ patterns in the digital data that are characteristic of the earliest stages of specific dementia-causing diseases.

We suggest applying the tools selected by the EDoN team together with the technologies funded by the Diagnostic Accelerator (DxA) as part of separate projects. This will allow us to compare and combine EDoN and DxA technologies to determine which combination of tools has the highest utility for early detection. After extensive testing, the most useful technologies will be integrated into an EDoN medical device with the aim of applying it in routine health checks. This will allow for the early detection of specific dementia-causing diseases in a cost-effective, low-burden manner on a population-wide scale, and will enable the development and targeted application of pharmaceutical interventions.