Alzheimer Disease, Alzheimer's Disease (Incl Subtypes), Mild Cognitive Impairment, Preclinical Alzheimer's Disease, Prodromal Alzheimer's Disease
Conditions
Keywords
Alzheimer's disease, Preclinical Alzheimer's disease, Prodromal Alzheimer's disease, Mild Cognitive Impairment, Normal Cognition, Amyloid, Speech, Acoustic, Language, Linguistic, Machine Learning, Artificial Intelligence
Brief summary
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, based on archival spoken or written language samples, as measured by the AUC of the receiver operating characteristic curve of the binary classifier distinguishing between amyloid positive and amyloid negative arms. Secondary objectives include (1) evaluating how many years before diagnosis of MCI such algorithms work, as measured on binary classifier performance of the classifiers trained to classify MCI vs cognitively normal (CN) arms using archival material from the following time bins before MCI diagnosis: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years; (2) evaluating at what age such algorithms can detect later amyloid positivity, as measured on binary classifier performance of the classifiers trained to classify amyloid positive vs amyloid negative arms using archival material from the following age bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old.
Interventions
None listed
Sponsors
Novoic Limited
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
50 Years to 85 Years
Healthy volunteers
Yes
Inclusion criteria
* Subjects are fully eligible for and have completed the AMYPRED (Amyloid Prediction in early stage Alzheimer's disease from acoustic and linguistic patterns of speech) study. (See https://clinicaltrials.gov/ct2/show/NCT04828122) * Subject has access to audio or written recordings created by them that are available for collection. * Subject consents to take part in PAST extension study.
Exclusion criteria
* Subject hasn't completed the full visit day in the AMYPRED study.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The primary outcome measure is the area under the curve (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. | Up to 85 years | Using archival spoken or written language samples as input. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The sensitivity, specificity and Cohen's kappa of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms using archival spoken or written language samples as input. | Up to 85 years | — |
| The AUC, sensitivity, specificity and Cohen's kappa of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms. | Up to 85 years | Using archival spoken or written language samples as input in the following bins: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years before MCI diagnosis. |
| The AUC, sensitivity, specificity and Cohen's kappa of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms. | Up to 85 years | Using archival spoken or written language samples as input in the following bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old. |
Countries
United Kingdom
Contacts
Primary ContactHead of Clinical Operations
Outcome results
None listed