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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).
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Arm 1: MCI amyloid positive |
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| Arm 2: MCI amyloid negative |
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| Arm 3: CN amyloid positive |
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| Arm 4: CN amyloid negative |
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| Measure | Description | Time Frame |
|---|---|---|
| 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 using speech recordings as input. | baseline |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | baseline | |
| The AUC, sensitivity, specificity and Cohen's kappa of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms. |
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Inclusion Criteria:
If taking part in the study through virtual visits, the following inclusion criteria also applies:
Exclusion Criteria:
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All participants of the study are based in the UK.
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| Name | Affiliation | Role |
|---|---|---|
| Emil Fristed, MSc | Novoic Limited | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Re:Cognition Health | Birmingham | B16 8LT | United Kingdom | |||
| Re:Cognition Health |
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| baseline |
| The AUC, sensitivity, specificity and Cohen's kappa of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms. | baseline |
| The AUC, sensitivity, specificity and Cohen's kappa of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms. | baseline |
| The agreement between the PACC5 composite and the corresponding regression model predicting it in all four Arms, as measured by the coefficient of individual agreement (CIA). | baseline |
| For each classifier/regressor in outcome 1-6, the correlation between the AUC/CIA and each age group, gender and speech-to-reverberation modulation energy ratio group, as measured by the Kendall rank correlation coefficient. | baseline |
| Guildford |
| GU2 7YD |
| United Kingdom |
| Re:Cognition Health | London | W1G 9JF | United Kingdom |
| Re:Cognition Health | Plymouth | PL68BT | United Kingdom |
| ID | Term |
|---|---|
| D000544 | Alzheimer Disease |
| D060825 | Cognitive Dysfunction |
| D013060 | Speech |
| D007802 | Language |
| ID | Term |
|---|---|
| D003704 | Dementia |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D024801 | Tauopathies |
| D019636 | Neurodegenerative Diseases |
| D019965 | Neurocognitive Disorders |
| D001523 | Mental Disorders |
| D003072 | Cognition Disorders |
| D014705 | Verbal Behavior |
| D003142 | Communication |
| D001519 | Behavior |
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