ReviewFrontiers in aging neuroscience2023
Noninvasive automatic detection of Alzheimer's disease from spontaneous speech: a review.
Review in Frontiers in aging neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 3 of them syntheses that pooled it.
What it found
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Who cites it
4 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Transformer-Based Deep Learning Approaches for Speech-Based Dementia Detection: A Systematic Review.IEEE journal of biomedical and health informatics · 2026Pooled it
- A systematic survey of the measures used to identify postoperative changes in language function following epilepsy surgery.Epilepsia open · 2025Pooled it
- Speech analysis for detecting depression in older adults: a systematic review.Frontiers in psychology · 2025Pooled it
- Neuropsychological Assessment Using Portable Automated Rapid Testing in Cognitively Healthy Older Adults: Test-Retest Reliability Study.JMIR formative research · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is considered as one of the leading causes of death among people over the age of 70 that is characterized by memory degradation and language impairment. Due to language dysfunction observed in individuals with AD patients, the speech-based methods offer non-invasive, convenient, and cost-effective solutions for the automatic detection of AD. This paper systematically reviews the technologies to detect the onset of AD from spontaneous speech, including data collection, feature extraction and classification. First the paper formulates the task of automatic detection of AD and describes the process of data collection. Then, feature extractors from speech data and transcripts are reviewed, which mainly contains acoustic features from speech and linguistic features from text. Especially, general handcrafted features and deep embedding features are organized from different modalities. Additionally, this paper summarizes optimization strategies for AD detection systems. Finally, the paper addresses challenges related to data size, model explainability, reliability and multimodality fusion, and discusses potential research directions based on these challenges.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.