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Development of Deep Learning Models for Detection of Neurodegenerative Diseases Using Speech - a Danish Language-based Artificial Intelligence Study (DetectAI)
The goal of this observational study is to learn if an artificial intelligence (AI)-based speech analysis tool can identify which patients with memory problems need specialist evaluation at a memory clinic. The main questions it aims to answer are: Can the AI model accurately distinguish between patients who need referral to a memory clinic (those with dementia or Mild Cognitive Impairment) and patients who don't (those with normal cognition or memory problems from other causes like depression)? Which speech patterns and cognitive test features are most useful for making this distinction? Researchers will compare speech recordings and cognitive test results from patients diagnosed with dementia or MCI to those from patients with normal cognition or non-neurodegenerative cognitive impairment to see if the AI model can reliably predict who needs specialist dementia care. Participants will: Complete standard cognitive tests at the memory clinic Perform structured speech tasks while being audio-recorded Receive their usual clinical evaluation and diagnosis from memory clinic specialists The results of this study will help develop a tool that can assist doctors in making faster, more accurate decisions about which patients need specialist dementia evaluation, potentially leading to earlier diagnosis and better patient outcomes.
Details
| Lead sponsor | Zealand University Hospital |
|---|---|
| Status | NOT_YET_RECRUITING |
| Enrolment | 440 |
| Start date | 2026-06-01 |
| Completion | 2028-07 |
Conditions
- Dementia (Diagnosis)
- Alzheimer Dementia (AD)
- Vascular Dementia (VaD)
- Lewy Body Dementia (LBD)
- Frontotemporal Dementia (FTD)
- Mild Cognitive Impairment (MCI)
- Depression - Major Depressive Disorder
- Stress
- Cognitive Impairment
Interventions
- Mini-mental State Examination
- Addenbrooke's Cognitive Examination
- Speech Task - Picture Description
- Speech Task - Picture Recall
- MRI
- blood sampling
- Depression screening
- Somatic- and neurological examination
- Speech Task - Picture Narrative
Primary outcomes
- Model A: Primary measure is the AUC-ROC of the model in distinguishing between MCI and AD as well as between MCI and cognitively healthy control participants. — At baseline (speech recording)
We will measure the AUR-ROC of AI predictions compared to clinical consensus diagnosis. Metrics will be presented including uncertainty estimates. Model performance will be measured on an independent test-set consisting of patients from the model B training population.
Countries
Denmark