The goal of this observational study is to develop and test an artificial intelligence (AI) model that can detect signs of dementia and related conditions from speech recordings. The main question is whether a speech-based AI model can correctly tell apart people with normal memory and thinking from those with cognitive impairment. The study will also explore whether the AI can distinguish dementia from depression, separate different dementia subtypes, and identify which people with Mild Cognitive Impairment (MCI) are likely to develop dementia. Participants will complete short memory and speech tasks while being recorded. The AI model will analyze these recordings to learn patterns linked to different diagnoses. At the end of the study, its accuracy will be tested on new participants.
Background Dementia is a growing public health challenge, and early and accurate diagnosis is essential for effective care and potential future disease-modifying treatments. Current diagnostic pathways are resource-intensive and associated with long waiting times. Speech reflects cognitive functioning, and recent international studies have shown that AI can detect dementia-related patterns in speech recordings with promising accuracy. This study aims to develop and validate a speech-based AI model in a Danish setting, providing a non-invasive and scalable screening tool for use in primary care. Phases one This protocol describes the first phase of our study which is expected to be completed in two separate phases. In phase one we seek to train an AI model to analyse speech data from participants with cognitive impairment and compare it to speech data from healthy control participants, as is detailed through this protocol. If the method is validated, we will continue to phase two. Future work In phase two we expect to conduct an external validation. The AI model analysis will be performed on 200 participants in the primary care sector referred for dementia evaluation. The results of the AI analysis will be compared against the final gold standard consensus diagnosis. Phase two will have a separate protocol which will be worked up based on the results from phase one. Elaboration of time perspective Other: Hybrid design. Most participants will be included in a cross-sectional case-control study (single speech recording). For participants with MCI, follow-up data will be collected within the study period to assess progression to dementia, allowing evaluation of the model's ability to distinguish progressive from non-progressive MCI.
Study Type
OBSERVATIONAL
Enrollment
340
Participants will be recorded during the test in order til allow the AI to learn and analyze speech patterns.
Participants will be recorded during the test in order til allow the AI to learn and analyze speech patterns.
Participants 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.
Participants will be asked to retell the fairy-tale "Cinderella", based on pictures that summarize the fairy-tale. In case "Cinderella" is not known, participants are asked to tell a story with a start, a middle and an end based on the provided pictures. The task will take 4 minutes. Participants will be recorded during the speech task in order to allow the AI to learn and analyze the speech patterns.
Two fluency tasks from the Addenbrooke's Cognitive Examination. First, the participant is asked to name as many animals as possible within 1 minutes. Next the participant is asked to name as many words starting with "S" as possible within 1 minute. Participants will be recorded during the tasks in order to allow the AI to learn and analyze speech patterns.
For 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 participants in the follow-up or new referral cohorts, imaging will be performed as part of the standard diagnostic battery and results will be obtained from the electronic journal.
Healthy 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 participants in the follow-up or new referral cohorts, blood sampling will be performed as part of the standard diagnostic battery and results will be obtained from the electronic journal.
Performed 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 participants in the follow-up or new referral cohorts, depression screening will be performed as part of the standard diagnostic battery and results will be obtained from the electronic journal.
Healthy 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 participants in the follow-up or new referral cohorts, a somatic and neurological examination will be performed as part of the standard diagnostic battery and results will be obtained from the electronic journal
Zealand University Hospital
Roskilde, Region Sjælland, Denmark
Accuracy of AI model in classifying cognitive impairment vs. unimpaired cognition
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.
Time frame: At baseline (speech recording)
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.
Time frame: At baseline (speech recording)
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.
Time frame: At baseline (speech recording) and up to 12 months after enrollment (to determine progression)
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.
Time frame: At baseline (speech recording)
Comparison with established biomarkers
Differences in diagnostic accuracy between AI predictions and state-of-the-art biomarkers for dementia diagnosis
Time frame: At baseline, or at time of biomarker testing if performed after baseline
Feature importance analysis
Feature importance will be evaluated using interpretability analyses (e.g. permutation importance, SHAP values, and/or ablation of feature groups) to quantify the contribution of acoustic and linguistic features to the model's predictions.
Time frame: At baseline (speech recording)
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