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N. Tavabi, D. Stück, A. Signorini, C. Karjadi, T. Al Hanai, M. Sandoval, C. Lemke, J. Glass, S. Hardy, M. Lavallee, B. Wasserman, T.F.A. Ang, C.M. Nowak, R. Kainkaryam, L. Foschini, R. Au

J Prev Alz Dis 2022;4(9):791-800

BACKGROUND: Although patients with Alzheimer’s disease and other cognitive-related neurodegenerative disorders may benefit from early detection, development of a reliable diagnostic test has remained elusive. The penetration of digital voice-recording technologies and multiple cognitive processes deployed when constructing spoken responses might offer an opportunity to predict cognitive status. Objective: To determine whether cognitive status might be predicted from voice recordings of neuropsychological testing Design: Comparison of acoustic and (para)linguistic variables from low-quality automated transcriptions of neuropsychological testing (n = 200) versus variables from high-quality manual transcriptions (n = 127). We trained a logistic regression classifier to predict cognitive status, which was tested against actual diagnoses. Setting: Observational cohort study. Participants: 146 participants in the Framingham Heart Study. Measurements: Acoustic and either paralinguistic variables (e.g., speaking time) from automated transcriptions or linguistic variables (e.g., phrase complexity) from manual transcriptions. Results: Models based on demographic features alone were not robust (area under the receiver-operator characteristic curve [AUROC] 0.60). Addition of clinical and standard acoustic features boosted the AUROC to 0.81. Additional inclusion of transcription-related features yielded an AUROC of 0.90. Conclusions: The use of voice-based digital biomarkers derived from automated processing methods, combined with standard patient screening, might constitute a scalable way to enable early detection of dementia.

N. Tavabi ; D. Stück ; A. Signorini ; C. Karjadi ; T. Al Hanai ; M. Sandoval ; C. Lemke ; J. Glass ; S. Hardy3 ; M. Lavallee ; B. Wasserman ; T.F.A. Ang ; C.M. Nowak ; R. Kainkaryam ; L. Foschini ; R. Au ; (2022): Cognitive Digital Biomarkers from Automated Transcription of Spoken Language. The Journal of Prevention of Alzheimer’s Disease (JPAD).


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