Amyloid Prediction in Early Stage Alzheimer's Disease Through Speech Phenotyping

Completed

Conditions studied: Alzheimer Disease, Preclinical Alzheimer's Disease, Prodromal Alzheimer's Disease, Alzheimer's Disease (Incl Subtypes), Mild Cognitive Impairment

In brief

The primary objective of the study is to evaluate whether a set of algorithms analysing acoustic and linguistic patterns of speech can detect amyloid-specific cognitive impairment in early stage Alzheimer's disease, as measured by the AUC of the receiver operating characteristic (ROC) curve of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms. Secondary objectives include (1) evaluating whether similar algorithms can detect amyloid-specific cognitive impairment in the cognitively normal (CN) and MCI Arms respectively, as measured on binary classifier performance; (2) whether they can detect MCI, as measured on binary classifier performance (AUC, sensitivity, specificity, Cohen's kappa), and the agreement between the PACC5 composite and the corresponding regression model predicting it in all Arms pooled (Wilcoxon signed-rank test, CIA); (3) evaluating variables that can impact performance of such algorithms of covariates from the speaker (age, gender, education level) and environment (measures of acoustic quality).

Key facts

Study ID
NCT04928976
Run by
Novoic Limited
People needed
67
Starts
2021-01-22
Expected to finish
2021-07-30
Last updated by the study team
2021-09-05

Who can join

Age: 50 and older, up to 85. Sex: any. Healthy volunteers: accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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