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

Zealand University Hospital

Status: Invitation-only

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

Treatments: Other: Somatic- and neurological examination; Diagnostic Test: MRI; Other: Fluency tasks; Other: Speech task - Picture Description; Diagnostic Test: blood sampling; Diagnostic Test: Addenbrooke's Cognitive Examination; Diagnostic Test: Depression screening; Diagnostic Test: Mini-mental State Examination; Other: Speech task - Story Retellling

Study type: Observational

Funder types: Other

Identifiers

NCT07200739

SJ-1107

Details and patient eligibility

About

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.

Full 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.

Enrollment: 340 estimated patients

Sex: All

Ages: 50+ years old

Volunteers: Accepts Healthy Volunteers

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

Trial design

340 participants in 3 patient groups

Healthy Controls

Description:

n\~40

Treatment:

Other: Speech task - Story Retellling

Diagnostic Test: Depression screening

Diagnostic Test: Mini-mental State Examination

Diagnostic Test: Addenbrooke's Cognitive Examination

Diagnostic Test: blood sampling

Other: Speech task - Picture Description

Other: Somatic- and neurological examination

Diagnostic Test: MRI

New Referrals

Description:

New Referrals to the memory clinic. Diagnosis unknown at the time of enrollment. N\~200

Treatment:

Other: Speech task - Story Retellling

Diagnostic Test: Mini-mental State Examination

Diagnostic Test: Addenbrooke's Cognitive Examination

Other: Speech task - Picture Description

Participants from follow-up

Description:

Participants with a clinical consensus diagnosis of either Alzheimer's dementia, Vascular dementia, Lewy body Dementia, Frontotemporal Dementia, Mild Cognitive Impairment or Depression. Diagnosis must be established in the memory clinic at ZUH within the past 6 months at the time of enrollment. N\~100

Treatment:

Other: Speech task - Story Retellling

Diagnostic Test: Mini-mental State Examination

Other: Fluency tasks

Other: Speech task - Picture Description