ArticleJournal of Alzheimer's disease : JAD2021
Automatic Subtyping of Individuals with Primary Progressive Aphasia.
Article in Journal of Alzheimer's disease : JAD, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 42 citations in OpenAlex.
- Deep learning-derived measures of sound-level accuracy in primary progressive apraxia of speech: A feasibility pipeline with descriptive evidence from two cases.Clinical linguistics & phonetics · 2026Article
- Neuropathology-specific language features in primary progressive aphasia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Identifying neuropathologic disease in primary progressive aphasia using narrative speech.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- How artificial intelligence is shaping neuropsychology: A focus on cognitive assessment of neurodegenerative disorders.Journal of neuropsychology · 2026Review
- Language biomarker screening using AI: a transdiagnostic approach to the brain.Scientific reports · 2026Article
- Verbal learning in logopenic variant Primary Progressive Aphasia: An EEG investigation.Neurobiology of aging · 2026Article
- Artificial intelligence-based speech error detection to differentiate primary progressive aphasia variants.Brain communications · 2026Article
- Written picture descriptions distinguish variants of primary progressive aphasia.Journal of Alzheimer's disease : JAD · 2025Article
- An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications.Perspectives of the ASHA special interest groups · 2025Article
- Article
- The use of low-density EEG for the classification of PPA and MCI.Frontiers in human neuroscience · 2025Article
- Role of pause duration in primary progressive aphasia.Aphasiology · 2025Article
- Protocol for a multisite study on the efficacy of transcranial direct current stimulation as an adjuvant to naming and spelling therapy in the treatment of oral and written naming in individuals with primary progressive aphasia.Frontiers in human neuroscience · 2025Article
- Primary Progressive Aphasia in Italian and English: A Cross-Linguistic Cohort Study.Neurology · 2024Observational
- Artificial intelligence classifies primary progressive aphasia from connected speech.Brain : a journal of neurology · 2024Article
- Connected Speech Fluency in Poststroke and Progressive Aphasia: A Scoping Review of Quantitative Approaches and Features.American journal of speech-language pathology · 2024Article
- Machine Learning Classification of Patients with Amnestic Mild Cognitive Impairment and Non-Amnestic Mild Cognitive Impairment from Written Picture Description Tasks.Brain sciences · 2024Article
- Autism Detection in Children: Integrating Machine Learning and Natural Language Processing in Narrative Analysis.Behavioral sciences (Basel, Switzerland) · 2024Article
- Discriminating nonfluent/agrammatic and logopenic PPA variants with automatically extracted morphosyntactic measures from connected speech.Cortex; a journal devoted to the study of the nervous system and behavior · 2024Article
- Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI.Frontiers in neuroscience · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe 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.
objectiveThe 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.
methodsIn 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.
resultsThe 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.
conclusionWe 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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