대한치매학회지 (Dementia and Neurocognitive Disorders)

대한치매학회 (Korean Dementia Association)
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Harnessing the Power of Voice: A Deep Neural Network Model for Alzheimer's Disease Detection

  • Chan-Young Park (Department of Neurology, Chung-Ang University College of Medicine) ;
  • Minsoo Kim (Research and Development, Baikal AI Inc.) ;
  • YongSoo Shim (Department of Neurology, Eunpyeong St. Mary's Hospital, The Catholic University of Korea) ;
  • Nayoung Ryoo (Department of Neurology, Eunpyeong St. Mary's Hospital, The Catholic University of Korea) ;
  • Hyunjoo Choi (Department of Communication Disorders, Korea Nazarene University) ;
  • Ho Tae Jeong (Department of Neurology, Chung-Ang University College of Medicine) ;
  • Gihyun Yun (Research and Development, Baikal AI Inc.) ;
  • Hunboc Lee (Research and Development, Baikal AI Inc.) ;
  • Hyungryul Kim (Research and Development, Baikal AI Inc.) ;
  • SangYun Kim (Department of Neurology, Seoul National University College of Medicine and Seoul National University Bundang Hospital) ;
  • Young Chul Youn (Department of Neurology, Chung-Ang University College of Medicine)
  • 투고 : 2023.10.11
  • 심사 : 2023.12.08
  • 발행 : 2024.01.31
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초록

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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과제정보

This research was supported by grants from the Ministry of SMEs and Startups (Project Number: S3079103) and the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (Project Number: NRF2017S1A6A3A01078538).

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