Dementia (Diagnosis)DetectAINCT07200739
Speech-Based Artificial Intelligence for Detection of Dementia in Danish Patients (DetectAI)
An observational study of Dementia (Diagnosis) and Alzheimer Dementia (AD), run by Zealand University Hospital at 1 site in Denmark.
Trial 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. Read more about this study
- Who is it for?
- Ages 50 years and olderHealthy volunteers can take part
- Conditions the study is for
- Dementia (Diagnosis), Alzheimer Dementia (AD), Vascular Dementia (VaD)
- What is being tested?
- Mini-mental State Examination, Addenbrooke's Cognitive Examination and Speech Task - Picture Description
- How many people
- 440 participants
- Where
- 1 siteDenmark
- Study dates
- 1 Nov 2026to Dec 2028
- Phase
- Observational
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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)
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- 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
Who cannot join
- 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
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- 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.
- Model B:
- Significantly impaired vision or hearing (to the extent that the patient cannot participate in the AI analysis)
- MMSE score < 16
- Patients unable to give consent
- Patients with concomitant psychosis or severe psychiatric comorbidities other than depression
- Any history of speech or language impairment predating the current condition
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Denmark1 site
Zealand University Hospital
Roskilde, Region Sjælland, 4000
Sofie J Vængebjerg, MD, PhD student, +45 30294621, sova@regsj.dk
Peter Høgh, MD, PhD, Assoc Prof, phh@regionsjaelland.dk
About this study
Official title: Development of Deep Learning Models for Detection of Neurodegenerative Diseases Using Speech - a Danish Language-based Artificial Intelligence Study (DetectAI)
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?
Read more
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.
Read the 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 machine learning models can detect dementia-related patterns in speech recordings with promising accuracy. This study aims to develop a speech-based deep learning model in a Danish setting, providing a non-invasive and scalable screening tool for use in primary care.
Study Design and Sampling Methods
This is an observational, cross-sectional study. Participants are recruited using two different sampling strategies corresponding to two artificial intelligence (AI) model development tracks:
Track A (Model A) - Retrospective case-control sampling:
This track addresses a focused diagnostic task: identification of Mild Cognitive Impairment (MCI). Participants are patients with a recent diagnosis from the memory clinic at Region Zealand University Hospital (ZUH). Sampling uses convenience sampling prioritizing patients who live close to the hospital, as data collection occurs during home visits. Patients with more recent diagnoses are prioritized to minimize the risk that participants have progressed to a new disease stage since diagnosis (e.g., from MCI to dementia).
Track B (Model B) - Prospective consecutive sampling:
This track uses prospective inclusion of newly referred patients to the memory clinic without pre-selection by diagnosis, reflecting a real-world clinical screening population. All eligible, consenting patients are included consecutively at their first clinic visit, before final diagnosis is established.
Model Development Following Best Practice Guidelines The study follows TRIPOD-AI (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis - Artificial Intelligence) and PROBAST-AI (Prediction model Risk Of Bias ASsessment Tool - Artificial Intelligence) guidelines for developing and validating clinical prediction models.
Key methodological features include:
Transparent model development: All preprocessing steps, feature extraction methods, model architectures, and hyperparameters will be documented Robust validation strategy: Data will be split into training, validation, and hold-out test sets for in-depth internal validation.
Minimizing bias: Participant selection, predictor measurement, outcome determination, and statistical analysis are designed to minimize bias according to PROBAST-AI domains Clinically relevant performance metrics: Sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC), positive and negative predictive values, and calibration Interpretability: Feature importance analysis to understand which speech characteristics contribute to predictions
Data Collection Speech data is collected through structured tasks including picture description, verbal fluency tests, story recall, and spontaneous speech. Audio is recorded using standardized equipment with quality control checks. Clinical diagnoses are established by experienced clinicians at the memory clinic following international diagnostic criteria.
- Conditions
- Dementia (Diagnosis), Alzheimer Dementia (AD), Vascular Dementia (VaD), Lewy Body Dementia (LBD), Frontotemporal Dementia (FTD), Mild Cognitive Impairment (MCI)
- Being tested
- Mini-mental State Examination, Addenbrooke's Cognitive Examination, Speech Task - Picture Description, Speech Task - Picture Recall, MRI
- 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
- Medical subject headings
- Dementia, Disease, Alzheimer Disease, Dementia, Vascular, Lewy Body Disease, Frontotemporal Dementia, Cognitive Dysfunction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Mini-mental State ExaminationDiagnostic test
Also called MMSE
Participants will be recorded during the test in order til allow the AI to learn and analyze speech patterns.
Addenbrooke's Cognitive ExaminationDiagnostic test
Also called ACE
Participants will be recorded during the test in order til allow the AI to learn and analyze speech patterns.
Speech Task - Picture DescriptionOther
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.
Speech Task - Picture RecallOther
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.
MRIDiagnostic 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.
blood samplingDiagnostic 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.
Depression screeningDiagnostic 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.
Somatic- and neurological examinationOther
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
Speech Task - Picture NarrativeOther
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
| Study group | Role | What they receive |
|---|---|---|
| Cognitively Healthy Control Participants for Model A | We seek to enroll 40 age-matched cognitively healthy control participants for the training of model A. Receives: 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. Receives: 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. Receives: 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 |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: At baseline (speech recording)
Secondary outcomes (5)
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.
Measured: 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.
Measured: 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.
Measured: At baseline (speech recording)
Comparison with established biomarkers
Differences in diagnostic accuracy between AI predictions and state-of-the-art biomarkers for dementia diagnosis
Measured: 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.
Measured: At baseline (speech recording)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Oct 2025
- Study start1 Nov 2026 (estimated)
- Primary completionSep 2028 (estimated)
- Study completionDec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for Dementia (Diagnosis)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 440 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Zealand University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- DemensAI ApS (private tech partner, Denmark)
- Responsible party
- Sponsor
- Investigators
- Peter Høgh, MD, PhD, Assoc Prof, Principal investigator, Zealand University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07200739
- Sponsor's protocol ID
- SJ-1107
- Acronym
- DetectAI
- Status last verified
- Jan 2026
- First submitted
- 23 Sep 2025
- Last updated
- 2 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
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.
PubMed 36791255Li 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.
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.
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.
PubMed 38957540Bex T. Comprehensive Guide to Multiclass Classification With Sklearn. Towards Data Science. 2021.
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.
PubMed 8487525Buderer 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.
PubMed 8870764Chen 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.
PubMed 35493062Agbavor 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.
PubMed 36812634de 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.
PubMed 33185605