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

NCT07200739 | Not Yet Recruiting

• 1 other identifier: SJ-1107
• Study Type: observational
• Target: 440
• Locations: 1 country
• Sites: 1

Brief Summary

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.

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

Keywords

artificial intelligence; speech-based artificial intelligence; artificial intelligence in dementia diagnostics; artificial intelligence for dementia screening; artificial intelligence for dementia classification; speech based artificial intelligence; dementia; Vascular dementia (VaD); Alzheimer dementia (AD); Lewy Body Dementia (LBD); Frontotemporal Dementia (FTD); Mild Cognitive Impairment (MCI); Depression - Major Depressive disorder; Dementia (diagnosis); machine learning; stress; Cognitive Impairment; deep learning

Outcome Measures

Primary Outcomes (1)

• 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.

  At baseline (speech recording)

Secondary Outcomes (5)

• Accuracy for dementia vs. depression

  At baseline (speech recording)

• 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)

• Classification of dementia subtypes (AD, VaD, LBD, FTD)

  At baseline (speech recording)

• Comparison with established biomarkers

  At baseline, or at time of biomarker testing if performed after baseline

• Feature importance analysis

  At baseline (speech recording)

Other Outcomes (2)

• Contribution of individual speech tasks to AI model performance

  At baseline (speech recording)

• Number of tasks required for optimal accuracy

  At baseline (speech recording)

Study Arms (3)

Cognitively Healthy Control Participants for Model A

We seek to enroll 40 age-matched cognitively healthy control participants for the training of model A.

Diagnostic Test: Mini-mental State Examination; Diagnostic Test: Addenbrooke's Cognitive Examination; Other: Speech Task - Picture Description; Other: Speech Task - Picture Recall; Diagnostic Test: MRI; Diagnostic Test: blood sampling; Diagnostic Test: Depression screening; Other: Somatic- and neurological examination; Other: Speech Task - Picture Narrative

Patient Participants for Model A

We seek to retrospectively enroll patients from the ZUH memory clinic with a diagnosis of either Alzheimer's Disease (AD, n=50) or MCI (n=50), made within 6 months prior to enrollment. These participants will be used for the training of model A.

Diagnostic Test: Mini-mental State Examination; Diagnostic Test: Addenbrooke's Cognitive Examination; Other: Speech Task - Picture Description; Other: Speech Task - Picture Recall; Other: Speech Task - Picture Narrative

Patient Participants for Model B

We will prospectively recruit newly referred patients for the memory clinic at ZUH. Enrollment happens at first patient visit. At this time, diagnosis is not yet known, but assumed present.

Diagnostic Test: Mini-mental State Examination; Diagnostic Test: Addenbrooke's Cognitive Examination; Other: Speech Task - Picture Description; Other: Speech Task - Picture Recall; Other: Speech Task - Picture Narrative

Interventions

Mini-mental State Examination DIAGNOSTIC_TEST

Participants will be recorded during the test in order til allow the AI to learn and analyze speech patterns.

Also known as: MMSE

Cognitively Healthy Control Participants for Model A; Patient Participants for Model A; Patient Participants for Model B

Addenbrooke's Cognitive Examination DIAGNOSTIC_TEST

Participants will be recorded during the test in order til allow the AI to learn and analyze speech patterns.

Also known as: ACE

Cognitively Healthy Control Participants for Model A; Patient Participants for Model A; Patient Participants for Model B

Speech Task - Picture Description OTHER

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.

Cognitively Healthy Control Participants for Model A; Patient Participants for Model A; Patient Participants for Model B

MRI DIAGNOSTIC_TEST

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 patient participants, imaging will be performed as part of the standard diagnostic battery and results will be obtained from the electronic journal.

Cognitively Healthy Control Participants for Model A

blood sampling DIAGNOSTIC_TEST

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 patient participants, blood sampling will be performed as part of the standard diagnostic battery and results will be obtained from the electronic journal.

Cognitively Healthy Control Participants for Model A

Depression screening DIAGNOSTIC_TEST

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 patient participants, depression screening will be performed as part of the standard diagnostic battery and results will be obtained from the electronic journal.

Cognitively Healthy Control Participants for Model A

Somatic- and neurological examination OTHER

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 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

Cognitively Healthy Control Participants for Model A

Speech Task - Picture Recall OTHER

Participants 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.

Cognitively Healthy Control Participants for Model A; Patient Participants for Model A; Patient Participants for Model B

Speech Task - Picture Narrative OTHER

The 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

Cognitively Healthy Control Participants for Model A; Patient Participants for Model A; Patient Participants for Model B

Eligibility Criteria

• Age: 50 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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.

You may qualify if:

• Model A (patient participants)
• 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
• Model A (cognitively healthy controls)
• Age \> 50 years
• Fluent in Danish
• Minimum of 7 years of schooling
• Model B:
• Age \> 50 years
• Fluent in Danish
• Minimum of 7 years of schooling

You may not qualify if:

• Model A:
• Patients:
• 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
• Patients with alcohol consumption \>21 standard alcohol units per week
• Any history of speech or language impairment predating the current condition
• Cognitively healthy controls:
• Significantly impaired vision or hearing (to the extent that the patient cannot participate in the AI analysis)
• MMSE \< 26 and ACE \< 90
• Clinical, laboratory, or neuroradiological findings that could affect cognitive functions
• Known diseases which are expected to impair cognitive functions
• Any history of speech or language impairment predating the current condition
• Patients with alcohol consumption \>21 standard alcohol units per week.

Sponsors & Collaborators

• Zealand University Hospital: lead
• DemensAI ApS (private tech partner, Denmark): collaborator

Study Sites (1)

Zealand University Hospital

Roskilde, Region Sjælland, 4000, Denmark

Location

Related Publications (11)

• Dargaud L, Partal A, Birn A, & Detlefsen S. N. (2023). Developing a Spontaneous Speech-based Artificial Intelligence for Alzheimer's Disease Detection. Transatlantic Telehealth Research Network (TTRN) International Scientific Conference 2023, Journal of the International Society for Telemedicine and eHealth.

  BACKGROUND

• Lanzi AM, Saylor AK, Fromm D, Liu H, MacWhinney B, Cohen ML. DementiaBank: Theoretical Rationale, Protocol, and Illustrative Analyses. Am J Speech Lang Pathol. 2023 Mar 9;32(2):426-438. doi: 10.1044/2022_AJSLP-22-00281. Epub 2023 Feb 15.

  PMID: 36791255 BACKGROUND

• Li J, Song K, Zheng B, Li D, Wu X, Meng H. Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection. arXiv preprint. 2023.

  BACKGROUND

• Luz S, Haider F, de la Fuente Garcia S, Fromm D, MacWhinney B. Detecting cognitive decline using speech only: The ADReSSo challenge. arXiv preprint 2021.

  BACKGROUND

• Luz S, Haider F, Fromm D, Lazarou I, Kompatsiaris I, Macwhinney B. An Overview of the ADReSS-M Signal Processing Grand Challenge on Multilingual Alzheimer's Dementia Recognition Through Spontaneous Speech. IEEE Open J Signal Process. 2024;5:738-749. doi: 10.1109/ojsp.2024.3378595. Epub 2024 Mar 18.

  PMID: 38957540 BACKGROUND

• Bex T. Comprehensive Guide to Multiclass Classification With Sklearn. Towards Data Science. 2021.

  BACKGROUND

• Nicholas LE, Brookshire RH. A system for quantifying the informativeness and efficiency of the connected speech of adults with aphasia. J Speech Hear Res. 1993 Apr;36(2):338-50. doi: 10.1044/jshr.3602.338.

  PMID: 8487525 BACKGROUND

• Buderer NM. Statistical methodology: I. Incorporating the prevalence of disease into the sample size calculation for sensitivity and specificity. Acad Emerg Med. 1996 Sep;3(9):895-900. doi: 10.1111/j.1553-2712.1996.tb03538.x.

  PMID: 8870764 BACKGROUND

• Chen J, Ye J, Tang F, Zhou J. Automatic Detection of Alzheimer's Disease Using Spontaneous Speech Only. Interspeech. 2021 Aug-Sep;2021:3830-3834. doi: 10.21437/interspeech.2021-2002.

  PMID: 35493062 BACKGROUND

• Agbavor F, Liang H. Predicting dementia from spontaneous speech using large language models. PLOS Digit Health. 2022 Dec 22;1(12):e0000168. doi: 10.1371/journal.pdig.0000168. eCollection 2022 Dec.

  PMID: 36812634 BACKGROUND

• de la Fuente Garcia S, Ritchie CW, Luz S. Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review. J Alzheimers Dis. 2020;78(4):1547-1574. doi: 10.3233/JAD-200888.

  PMID: 33185605 BACKGROUND

Related Links

• Website of collaborator, DemensAI SPs (responsible party for training and testing the AI model)

MeSH Terms

Conditions

Dementia; Disease; Alzheimer Disease; Dementia, Vascular; Lewy Body Disease; Frontotemporal Dementia; Cognitive Dysfunction

Interventions

Blood Specimen Collection; Diploidy

Condition Hierarchy (Ancestors)

Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Neurocognitive Disorders; Mental Disorders; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Tauopathies; Neurodegenerative Diseases; Cerebrovascular Disorders; Intracranial Arteriosclerosis; Intracranial Arterial Diseases; Leukoencephalopathies; Arteriosclerosis; Arterial Occlusive Diseases; Vascular Diseases; Cardiovascular Diseases; Parkinsonian Disorders; Basal Ganglia Diseases; Movement Disorders; Synucleinopathies; Frontotemporal Lobar Degeneration; TDP-43 Proteinopathies; Proteostasis Deficiencies; Metabolic Diseases; Nutritional and Metabolic Diseases; Cognition Disorders

Intervention Hierarchy (Ancestors)

Specimen Handling; Clinical Laboratory Techniques; Diagnostic Techniques and Procedures; Diagnosis; Punctures; Surgical Procedures, Operative; Investigative Techniques; Ploidies; Genetic Phenomena

Study Officials

• Peter Høgh, MD, PhD, Assoc Prof

  Zealand University Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Sofie J Vængebjerg, MD

CONTACT

+4530294621; sova@regsj.dk

Peter Høgh, MD, PhD, Assoc Prof

CONTACT

+45 22526698; phh@regionsjaelland.dk

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 23, 2025
• First Posted: October 1, 2025
• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): May 1, 2028
• Study Completion (Estimated): July 1, 2028
• Last Updated: May 5, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

DK (1)