Automatic Subtyping of Individuals with Primary Progressive Aphasia
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Abstract
BACKGROUND: The classification of patients with primary progressive aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists. OBJECTIVE: The aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA. METHODS: In this paper, we present a machine learning model based on deep neural networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as to expert clinicians' classifications. RESULTS: The DNN model outperformed the other machine learning models as well as expert clinicians' classifications with 80% classification accuracy. Importantly, 90% of patients with nfvPPA and 95% of patients with lvPPA was identified correctly, providing reliable subtyping of these patients into their corresponding PPA variants. CONCLUSION: We show that the combined speech and language markers from connected speech productions can inform variant subtyping in patients with PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick, and inexpensive classification of patients with PPA.
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AI models were generally highly accurate in predicting primary progressive aphasia, especially for patients with reduced verbal fluency and fluency difficulties, but less accurate in discriminating patients with comprehension-related disorders.
“The models were generally highly accurate, especially for PPA patients with reduced verbal fluency and fluency difficulties, but less accurate in discriminating PPA patients with comprehension-related disorders.”
From Artificial intelligence in assessment and intervention of speech and language disorders: A literature review (2025) · cites this paper as [36]
AI models achieved a higher overall accuracy than three less-experienced speech-language pathologists in classifying primary progressive aphasia.
“The results showed that the AI models achieved a higher overall accuracy.”
From Artificial intelligence in assessment and intervention of speech and language disorders: A literature review (2025) · cites this paper as [36]
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Artificial intelligence classifies primary progressive aphasia from connected speech
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“Prior work has also implemented machine learning methods for differential diagnosis of PPA subtypes, including svPPA, nfvPPA and logopenic PPA (Agosta et al., 2015; Themistocleous et al., 2021).”
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- Authors
- Themistocleous, C., Ficek, B., Webster, K., den Ouden, D.-B., Hillis, A. E., & Tsapkini, K.
- Affiliations
- Johns Hopkins University (US)
- Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- Corresponding author
- Themistocleous, Charalambos
- Venue
- Journal of Alzheimer’s Disease
- Publisher
- SAGE Publications
- Published
- 2021-02-02
- Volume
- 79
- Issue
- 3
- Pages
- 1185-1194
- ISSN
- 1387-2877 (ISSN-L)
- 1875-8908
- PMC
- PMC7990416
- OpenAlex
- W3119573264
- Cited by
- 51
- References
- 35
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 16 · 2023: 6 · 2022: 3 · 2021: 4 · 2020: 1
- FWCI
- 5.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Alzheimer s Disease (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Chapman University Digital Commons (Chapman University) (submittedVersion, cc-by-nc)
- Funders
- Johns Hopkins University
- National Institutes of Health
- Science of Learning Institute, Johns Hopkins University
- National Institute on Aging (P30AG066507)
- National Institute on Deafness and Other Communication Disorders (R01DC014475)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Keywords
- primary progressive aphasia
- MeSH terms
- Machine Learning, Acoustics, Aged, Decision Trees, Female, Humans, Linguistics, Male, Models, Theoretical, Reproducibility of Results, Neural Networks, Computer, Aphasia, Primary Progressive, Primary Progressive Nonfluent Aphasia, Support Vector Machine
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