ArticleAlzheimer's & dementia (Amsterdam, Netherlands)
Automated differentiation of non-fluent and logopenic primary progressive aphasia in Italian speakers using acoustic and linguistic speech measures.
Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Automated differentiation of non-fluent and logopenic primary progressive aphasia in Italian speakers using acoustic and linguistic speech measures.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Differentiating the nonfluent/agrammatic and logopenic variants of primary progressive aphasia (PPA; nfvPPA and lvPPA, respectively) remains clinically challenging due to overlapping subtle speech and language impairments. We investigated whether automated connected speech analysis can support differential diagnosis. Methods: We analyzed connected speech from 84 Italian speakers with PPA (23 nfvPPA, 23 semantic variant [svPPA], and 38 lvPPA) using a picture-description task. Prosodic, phonological, and morphosyntactic features were extracted. Machine-learning classifiers were trained for binary (lvPPA vs. nfvPPA) and multiclass (lvPPA, nfvPPA, svPPA) classification. Explainability analyses identified key features. Results: The combination of speech and language features effectively discriminated among PPA variants. Binary classification reached 81.67% accuracy, while multiclass classification reached 63.86% accuracy. Noun rate, local jitter, articulation rate, and total pauses were among the most informative features. Discussion: Automated analysis of connected speech provides objective linguistic biomarkers that enhance diagnostic accuracy and support reliable differentiation of PPA variants in clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
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.