Early Detection of Alzheimer's Disease and Affective Disorders by Automated Voice and Speech Analysis (PLATA)

Early Detection of Alzheimer's Disease and Affective Disorders by Automated Voice and Speech Analysis

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05943834

Acronym

PLATA

Enrollment

100

Registered

2023-07-13

Start date

2023-07-13

Completion date

2027-10-10

Last updated

2026-03-19

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Neurocognitive Disorders

Keywords

Alzheimer's disease, Mood disorder

Brief summary

PLATA aims to develop an algorithm to identify vocal biomarkers of Alzheimer's dementia. Using data collected as part of routine care, speech patterns will be compared to known biomarkers of Alzheimer's disease, such as amyloid 1-42 and p-Tau in CSF (cerebrospinal fluid). If biomarkers of speech can be identified in Alzheimer's disease, it is possible that patients and research participants will no longer need to undergo need to undergo the intensive and invasive baseline biomarker methods currently used, such as lumbar punctures and PET scans.

Interventions

OTHERSeries of cognitive tasks during a semi-automated call

Tasks: * Verbal learning recall (immediate) or Story Recall task (immediate) * Narrative Storytelling /free speech * Verbal fluency task * Verbal learning recall (delayed) or Story Recall task (delayed)

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

50 Years to 100 Years

Healthy volunteers

Inclusion criteria

* Age ≥ 50 years * Diagnosis relevant biomarker and neuropsychological data already available * Cognitively healthy to very mild dementia (CDR score max. 0.5) * Sufficient knowledge of the study language to understand study information, non opposition form,and questionnaires * Expression of non opposition

Exclusion criteria

* Hearing problems * Patient protected by law, under guardianship or curator ship, or not able to participate in a clinical study according to the article L.1121-16 of the French Public Health Code

Design outcomes

Primary

Measure	Time frame	Description
Build and validate speech-based machine learning models for relevant Phenotype detection through access to phenotyped patients from reference memory center.	20 minutes	Speech biomarker algorithm(s)

Countries

France

Contacts

CONTACTEric ETTORE, MD

ettore.e@chu-nice.fr04.92.03.47.70

PRINCIPAL_INVESTIGATOREric ETTORE, MD

Centre Hospitalier Universitaire de Nice

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Mar 20, 2026