Dement Neurocogn Disord.
2024 Jan;23(1):1-10. 10.12779/dnd.2024.23.1.1.
Harnessing the Power of Voice: A Deep Neural Network Model for Alzheimer’s Disease Detection
- Affiliations
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- 1Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea
- 2Research and Development, Baikal AI Inc., Seoul, Korea
- 3Department of Neurology, Eunpyeong St. Mary’s Hospital, The Catholic University of Korea, Seoul, Korea
- 4Department of Communication Disorders, Korea Nazarene University, Cheonan, Korea
- 5Department of Neurology, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam, Korea
- 6Department of Medical Informatics, Chung-Ang University College of Medicine, Seoul, Korea
- KMID: 2552349
- DOI: http://doi.org/10.12779/dnd.2024.23.1.1
Abstract
- Background and Purpose
Voice, reflecting cerebral functions, holds potential for analyzing and understanding brain function, especially in the context of cognitive impairment (CI) and Alzheimer’s disease (AD). This study used voice data to distinguish between normal cognition and CI or Alzheimer’s disease dementia (ADD).
Methods
This study enrolled 3 groups of subjects: 1) 52 subjects with subjective cognitive decline; 2) 110 subjects with mild CI; and 3) 59 subjects with ADD. Voice features were extracted using Mel-frequency cepstral coefficients and Chroma.
Results
A deep neural network (DNN) model showed promising performance, with an accuracy of roughly 81% in 10 trials in predicting ADD, which increased to an average value of about 82.0%±1.6% when evaluated against unseen test dataset.
Conclusions
Although results did not demonstrate the level of accuracy necessary for a definitive clinical tool, they provided a compelling proof-of-concept for the potential use of voice data in cognitive status assessment. DNN algorithms using voice offer a promising approach to early detection of AD. They could improve the accuracy and accessibility of diagnosis, ultimately leading to better outcomes for patients.
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