ArticleBrain : a journal of neurology2024
Artificial intelligence classifies primary progressive aphasia from connected speech.
Article in Brain : a journal of neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
- Machine Learning-Based Cognitive Assessment With The Autonomous Cognitive Examination: Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- Extending standardized speech analysis across languages: An Italian toolkit for transcription and coding of neurological speech.Behavior research methods · 2026Article
- Neuropathology-specific language features in primary progressive aphasia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Characterizing Verb Argument Structure in Aphasia Using Dependency Parsers.Journal of speech, language, and hearing research : JSLHR · 2026Article
- Improving the clinical trial landscape for patients with atypical variants of Alzheimer's disease: a call to action.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Age-related determinants of geriatric depression: an analysis of theory-driven and data-driven approaches.BMC health services research · 2026Article
- Identifying neuropathologic disease in primary progressive aphasia using narrative speech.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.Seminars in neurology · 2026Review
- Predicting amyloid status in Primary Progressive Aphasia using explainable artificial intelligence.NPJ dementia · 2026Article
- Evaluating the utility of large language models for detecting and simulating language dysfunction.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence-based speech error detection to differentiate primary progressive aphasia variants.Brain communications · 2026Article
- Design considerations for C9orf72 disease prevention trials.Brain : a journal of neurology · 2025Review
- Navigating the Complexity of Bilingual Aphasia: Current Insights and Future Directions.Brain sciences · 2025Review
- Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis.Journal of medical Internet research · 2025Article
- Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses.Diagnostics (Basel, Switzerland) · 2025Review
- Large language models for disease diagnosis: a scoping review.NPJ artificial intelligence · 2025Review
- Rethinking the diagnosis of primary progressive aphasia: current challenges and future directions.Frontiers in aging neuroscience · 2025Article
- Language abnormalities in Alzheimer's disease indicate reduced informativeness.Annals of clinical and translational neurology · 2024Article
- Advanced AI techniques for classifying Alzheimer's disease and mild cognitive impairment.Frontiers in aging neuroscience · 2024Article
- Language uncovers visuospatial dysfunction in posterior cortical atrophy: a natural language processing approach.Frontiers in neuroscience · 2024Article
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Abstract
Neurodegenerative dementia syndromes, such as primary progressive aphasias (PPA), have traditionally been diagnosed based, in part, on verbal and non-verbal cognitive profiles. Debate continues about whether PPA is best divided into three variants and regarding the most distinctive linguistic features for classifying PPA variants. In this cross-sectional study, we initially harnessed the capabilities of artificial intelligence and natural language processing to perform unsupervised classification of short, connected speech samples from 78 pateints with PPA. We then used natural language processing to identify linguistic features that best dissociate the three PPA variants. Large language models discerned three distinct PPA clusters, with 88.5% agreement with independent clinical diagnoses. Patterns of cortical atrophy of three data-driven clusters corresponded to the localization in the clinical diagnostic criteria. In the subsequent supervised classification, 17 distinctive features emerged, including the observation that separating verbs into high- and low-frequency types significantly improved classification accuracy. Using these linguistic features derived from the analysis of short, connected speech samples, we developed a classifier that achieved 97.9% accuracy in classifying the four groups (three PPA variants and healthy controls). The data-driven section of this study showcases the ability of large language models to find natural partitioning in the speech of patients with PPA consistent with conventional variants. In addition, the work identifies a robust set of language features indicative of each PPA variant, emphasizing the significance of dividing verbs into high- and low-frequency categories. Beyond improving diagnostic accuracy, these findings enhance our understanding of the neurobiology of language processing.
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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 Socratic graph.