Related Experiment Video
Updated: Aug 24, 2025
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
Published on: July 24, 2019
Cognitive Digital Biomarkers from Automated Transcription of Spoken Language
N Tavabi1, D Stück, A Signorini
1Rhoda Au, 72 E. Concord Street, Boston University School of Medicine, Boston, MA 02118. Telephone: (617) 358-0089;
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.
