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.
Age > 50 years
Fluent in Danish
Minimum of 7 years of schooling
A diagnosis of either MCI or AD, given at the SUH memory clinic within 6 months before enrollment
Significantly impaired vision or hearing (to the extent that the patient cannot participate in the AI analysis)
MMSE score < 16
Concomitant diagnoses which are expected to influence cognitive impairment (eg. depression)
Patients unable to give consent
Participants are recruited from patients who are followed at- or referred to the memory clinic at Zealand University Hospital. Age and gender matched healthy controls for model A are recruited from the participants' relatives.
Other
Cross-sectional
Mini-mental State Examination
Diagnostic testParticipants will be recorded during the test in order til allow the AI to learn and analyze speech patterns.
Addenbrooke's Cognitive Examination
Diagnostic testParticipants will be recorded during the test in order til allow the AI to learn and analyze speech patterns.
Speech Task - Picture Description
Other interventionParticipants will be asked to describe the Cookie Theft Picture from the Boston Diagnostic Aphasia Examination. The task will take 2 minutes. Participants will be recorded during the speech task in order to allow the AI to learn and analyze the speech patterns.
Speech Task - Picture Recall
Other interventionParticipants will be asked to recall the picture shown in the previous speech task "Picture Narrative". This task will take 2 minutes. Participants will be recorded during the test in order til allow the AI to learn and analyze speech patterns.
MRI
Diagnostic testFor healthy controls an MRI will be conducted to provide comparable imaging and as part of screening to ensure they do not meet exclusion criteria (neuroradiological findings that could affect cognitive functions). For patient participants, imaging will be performed as part of the standard diagnostic battery and results will be obtained from the electronic journal.
blood sampling
Diagnostic testHealthy control participants will undergo a standard blood test panel commonly used in dementia diagnostics. The panel includes complete blood counts, inflammatory markers, kidney- and liver function markers, thyroid-stimulating hormone (TSH), vitamine B12 and folate. These tests are performed to exclude underlying medical conditions that could mimic cognitive impairment. For patient participants, blood sampling will be performed as part of the standard diagnostic battery and results will be obtained from the electronic journal.
Depression screening
Diagnostic testPerformed on healthy controls to rule out depression using either the geriatric depression scale (GDS) for patients \> 65 year of age or the Major Depression Index (MDI) for patiens \<65 year of age. For patient participants, depression screening will be performed as part of the standard diagnostic battery and results will be obtained from the electronic journal.
Somatic- and neurological examination
Other interventionHealthy controls will undergo a standard somatic and neurological examination to exclude conditions that may affect cognition. This includes basic neurological assessment and clinical evaluation of general health status. For patient participants, a somatic and neurological examination will be performed as part of the standard diagnostic battery and results will be obtained from the electronic journal
Speech Task - Picture Narrative
Other interventionThe participant is asked to tell a brief story based on a culturally neutral picture. This task will take approximately 2 minutes. Participants will be recorded during the speech task in order to allow the AI to learn and analyze the speech patterns
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.
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.
Accuracy for dementia vs. depression
Measured by sensitivity, specificity, AUR-ROC of AI predictions compared to clinical consensus diagnosis, using baseline speech recordings from participants. Model performance will be measured after database lock at study completion.
Sub-classification of Mild Cognitive Impairment (MCI) into progressive vs. non-progressive
Measured by sensitivity, specificity, AUR-ROC of AI predictions compared to clinical consensus diagnosis, using baseline speech recordings from participants. Model performance will be measured after database lock at study completion. Progression is defined as new dementia diagnosis during study period.
Classification of dementia subtypes (AD, VaD, LBD, FTD)
Measured by sensitivity, specificity, AUR-ROC of AI predictions compared to clinical consensus diagnosis, using baseline speech recordings from participants. Model performance will be measured after database lock at study completion.
Comparison with established biomarkers
Differences in diagnostic accuracy between AI predictions and state-of-the-art biomarkers for dementia diagnosis
Contribution of individual speech tasks to AI model performance
Contribution of individual speech tasks will be evaluated by comparing model performance (e.g. accuracy, sensitivity, specificity, AUC-ROC) when trained and tested on subsets of speech tasks (memory tests, story recall, picture description). This will identify which tasks provide the strongest diagnostic signal.
Number of tasks required for optimal accuracy
Evaluation of whether a reduced set of speech tasks provide accuracy comparable to the full test battery.
Click on the lead sponsor to view all of their trials.
Zealand University Hospital
Lead sponsor
DemensAI ApS (private tech partner, Denmark)
Collaborator
This trial is not recruiting at the moment. You can still explore other options:
