The Accuracy of Speech and Linguistic Analysis in Early Diagnostics of Neurocognitive Disorders in a Memory Clinic Setting.

Autor:	Ter Huurne D; Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands., Ramakers I; Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands., Possemis N; Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands., Banning L; Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands., Gruters A; Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands., Van Asbroeck S; Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands., König A; National Institute for Research in Computer Science and Automation (INRIA), Stars Team, Sophia Antipolis, France., Linz N; ki elements, Saarbrücken, Germany., Tröger J; ki elements, Saarbrücken, Germany., Langel K; Janssen Clinical Innovation, Beerse, Belgium., Verhey F; Maastricht University Medical Center+ (MUMC+), Department of Psychiatry and Psychology, Maastricht, the Netherlands., de Vugt M; Maastricht University Medical Center+ (MUMC+), Department of Psychiatry and Psychology, Maastricht, the Netherlands.
Jazyk:	angličtina
Zdroj:	Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists [Arch Clin Neuropsychol] 2023 Jul 25; Vol. 38 (5), pp. 667-676.
DOI:	10.1093/arclin/acac105
Abstrakt:	Objective: To investigate whether automatic analysis of the Semantic Verbal Fluency test (SVF) is reliable and can extract additional information that is of value for identifying neurocognitive disorders. In addition, the associations between the automatically derived speech and linguistic features and other cognitive domains were explored. Method: We included 135 participants from the memory clinic of the Maastricht University Medical Center+ (with Subjective Cognitive Decline [SCD; N = 69] and Mild Cognitive Impairment [MCI]/dementia [N = 66]). The SVF task (one minute, category animals) was recorded and processed via a mobile application, and speech and linguistic features were automatically extracted. The diagnostic performance of the automatically derived features was investigated by training machine learning classifiers to differentiate SCD and MCI/dementia participants. Results: The intraclass correlation for interrater reliability between the clinical total score (golden standard) and automatically derived total word count was 0.84. The full model including the total word count and the automatically derived speech and linguistic features had an Area Under the Curve (AUC) of 0.85 for differentiating between people with SCD and MCI/dementia. The model with total word count only and the model with total word count corrected for age showed an AUC of 0.75 and 0.81, respectively. Semantic switching correlated moderately with memory as well as executive functioning. Conclusion: The one-minute SVF task with automatically derived speech and linguistic features was as reliable as the manual scoring and differentiated well between SCD and MCI/dementia. This can be considered as a valuable addition in the screening of neurocognitive disorders and in clinical practice. (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu