Harnessing the Power of Voice: A Deep Neural Network Model for Alzheimer's Disease Detection.
Chan-Young ParkMinsoo KimYongSoo ShimNayoung RyooHyunjoo ChoiHo Tae JeongGihyun YunHunboc LeeHyungryul KimSang Yun KimYoung Chul YounPublished in: Dementia and neurocognitive disorders (2024)
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.
Keyphrases
- neural network
- end stage renal disease
- ejection fraction
- newly diagnosed
- machine learning
- prognostic factors
- deep learning
- electronic health record
- cognitive decline
- radiation therapy
- metabolic syndrome
- adipose tissue
- patient reported outcomes
- climate change
- locally advanced
- insulin resistance
- real time pcr
- rectal cancer