A systematic literature review of automatic Alzheimer’s disease detection from speech and language
Open accessAbstract
Objective: In recent years numerous studies have achieved promising results in Alzheimer’s Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research. Materials and Methods: We searched PubMed, Ovid, and Web of Science for articles published in English between 2013 and 2019. We performed a systematic literature review to answer 5 key questions: (1) What were the characteristics of participant groups? (2) What language data were collected? (3) What features of speech and language were the most informative? (4) What methods were used to classify between groups? (5) What classification performance was achieved? Results and Discussion: We identified 33 eligible studies and 5 main findings: participants’ demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups. Conclusion: The systematic literature review supported the argument that language and speech could successfully be used to detect dementia automatically. Future studies should aim for larger and more balanced datasets, combine data collection methods and the type of information analyzed, focus on the early stages of the disease, and report performance using standardized metrics.
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- Authors
- Petti, U., Baker, S., & Korhonen, A.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Petti, Ulla
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-09-14
- Volume
- 27
- Issue
- 11
- Pages
- 1784-1797
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC7671617
- OpenAlex
- W3087538850
- Cited by
- 168
- References
- 56
- Citations by year
- 2026: 24 · 2025: 30 · 2024: 33 · 2023: 36 · 2022: 26 · 2021: 17 · 2020: 2
- FWCI
- 3.36
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Medical Informatics Association (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- Economic and Social Research Council Cambridge Doctoral Training Partnership (ES/P000738/1)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- Alzheimer's disease detection, dementia detection, speech analysis, linguistic analysis, spontaneous speech, semantic impairment, syntactic impairment, mild cognitive impairment, disease progression detection, classification performance
- MeSH terms
- Machine Learning, Alzheimer Disease, Artificial Intelligence, Decision Trees, Humans, Language Disorders, Language Tests, Natural Language Processing, Speech
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