Introduction: Positron emission tomography (PET) amyloid imaging has become an important part of the diagnostic workup for patients with primary progressive aphasia (PPA) and uncertain underlying pathology. Here, we employ a semi-automated analysis of connected speech (CS) with a twofold objective. First, to determine if quantitative CS features can help select primary progressive aphasia (PPA) patients with a higher probability of a positive PET amyloid imaging result. Second, to examine the relevant group differences from a clinical perspective.
Methods: 117 CS samples from a well-characterised cohort of PPA patients who underwent PET amyloid imaging were collected. Expert consensus established PET amyloid status for each patient, and 40% of the sample was amyloid positive.
Results: Leave-one-out cross-validation yields 77% classification accuracy (sensitivity: 74%, specificity: 79%).
Discussion: Our results confirm the potential of CS analysis as a screening tool. Discriminant CS features from lexical, syntactic, pragmatic, and semantic domains are discussed.
Keywords: Alzheimer's disease; Biomarkers; Connected speech; Differential diagnosis; Natural language processing; Primary progressive aphasia; Telemedicine.
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