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Alzheimer Dementia (AD) / Dementia (diagnosis) / Speech Task - Picture Description · Observational Study
ClinicalTrials.gov · September 2, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a registry record for a planned observational study of AI-based speech analysis for dementia detection in Danish patients. No results have been posted; the study has not yet begun recruitment. The primary outcome is the AUC-ROC of an AI model distinguishing MCI and AD from cognitively healthy controls using speech tasks, cognitive tests, and auxiliary diagnostics.
Observational. Dementia (Diagnosis), Alzheimer Dementia (AD), Vascular Dementia (VaD), Lewy Body Dementia (LBD), Frontotemporal Dementia (FTD), Mild Cognitive Impairment (MCI…; age from 50 Years; accepts healthy volunteers. Intervention: Cognitively Healthy Control Participants for Model A; Patient Participants for Model A; Patient Participants for Model B. n = 440. 1 site: Denmark.
This is a registry record for a planned observational study of AI-based speech analysis for dementia detection in Danish patients. No results have been posted; the study has not yet begun recruitment. The primary outcome is the AUC-ROC of an AI model distinguishing MCI and AD from cognitively healthy controls using speech tasks, cognitive tests, and auxiliary diagnostics.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
This study aims to develop an AI tool to assist referral decisions for memory clinic evaluation. Once results are available, clinicians should assess whether speech-based AI can reliably and independently predict need for specialist dementia evaluation in primary care or memory clinic settings.
This is a registry record for a planned observational study with no results reported; recruitment has not yet begun, making it a prospective design specification only.
As stated by the source record.
Quoted from the source exactly as published.
This study aims to develop an AI tool to assist referral decisions for memory clinic evaluation. Once results are available, clinicians should assess whether speech-based AI can reliably and independently predict need for specialist dementia evaluation in primary care or memory clinic settings.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no key findings. That is a gap in the analysis, not a judgement about the study.
Registry record from ClinicalTrials.gov (NCT07200739). This is a study registration, not published results. Lead sponsor: Zealand University Hospital. Recruitment status: NOT_YET_RECRUITING. Study type: OBSERVATIONAL. Enrollment: 440 participants (ESTIMATED). 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. Interventions: 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. Primary outcome measures: 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. , At baseline (speech recording). 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.
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