Speech-Based Artificial Intelligence for Detection of Dementia in Danish Patients

Not yet recruiting

• Conditions: Dementia (Diagnosis); Lewy Body Dementia (LBD); Alzheimer Dementia (AD); Vascular Dementia (VaD); Frontotemporal Dementia (FTD); Mild Cognitive Impairment (MCI); Depression - Major Depressive Disorder

• Registration Number: NCT07200739
• Lead Sponsor: Zealand University Hospital

Brief Summary

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.

Detailed Description

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 Timeline

• Status Verified: 2025-09
• Study First Submitted: 2025-09-23
• Study First Submitted QC: 2025-09-23
• Last Update Submitted: 2025-09-23
• Study Start: 2025-09-29
• Study First Posted: 2025-10-01
• Last Update Posted: 2025-10-01
• Primary Completion: 2027-06
• Study Completion: 2028-06-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 340

Inclusion Criteria

• Age > 50 years
• Fluent in Danish
• Minimum 7 years of schooling

For participants from the follow-up cohort:

• A consensus diagnosis of either AD, VaD, LBD, FTD, MCI or depression established at the memory clinic within 6 months prior to enrollment

For participants from the healthy control cohort:

• No known cognitive impairment or affective disorder

Exclusion Criteria

• Significantly impaired vision or hearing (to the extent that the participant cannot participate in the linguistic AI analysis)
• Participants unable to give consent

Participants from follow-up and new referrals cohort:

• MMSE score < 16
• Participants with multiple diagnoses (eg. mixed dementia or AD with concurrent depression)

For participants from the new referrals cohort:

• Participants falling outside of the six categories included in the study (AD, VaD, LBD, FTD, MCI, Depression)
• Participants where it is obvious at baseline that they will not fall within the above categories (can be excluded before clinical consensus diagnosis is given)

For participants from the healthy control cohort:

• MMSE <26 and ACE <90
• GDS score indicating depression (6 or higher)
• Clinical, laboratory or neuroradiological findings that could affect cognitive functions

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Accuracy of AI model in classifying cognitive impairment vs. unimpaired cognition	At baseline (speech recording)	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.

Secondary Outcome Measures

Name	Time	Method
Accuracy for dementia vs. depression	At baseline (speech recording)	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	At baseline (speech recording) and up to 12 months after enrollment (to determine progression)	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)	At baseline (speech recording)	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	At baseline, or at time of biomarker testing if performed after baseline	Differences in diagnostic accuracy between AI predictions and state-of-the-art biomarkers for dementia diagnosis
Feature importance analysis	At baseline (speech recording)	Model interpretability analyses will highlight which speech features contribute most strongly to classification

Trial Locations

Locations (1)

Zealand University Hospital; 🇩🇰 Roskilde, Region Sjælland, Denmark