ArticleNPJ dementia2026
Predicting amyloid status in Primary Progressive Aphasia using explainable artificial intelligence.
Article in NPJ dementia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
6 authors.
Funding
Abstract
Recent success has been achieved in Alzheimer's disease (AD) therapeutics targeting amyloid beta (Aβ), demonstrating a significant reduction in rates of cognitive decline. However, testing methods for Aβ positivity are costly, which motivates the development of accessible approaches to promptly steer patients toward appropriate diagnostic tests. Here, we employ a pre-trained language model (Distil-RoBERTa) to predict Aβ positivity from short, connected speech samples. We further use explainable artificial intelligence (XAI) methods to extract interpretable linguistic features that can be employed in clinical practice. We obtained language samples from 71 patients with Primary Progressive Aphasia across its three variants. Aβ positivity was established through the analysis of cerebrospinal fluid, amyloid PET, or autopsy. 51% of the participants were amyloid-positive. We trained Distil-RoBERTa for 16 epochs with a batch size of 6 and a learning rate of 5e
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