The Accuracy of Speech and Linguistic Analysis in Early Diagnostics of Neurocognitive Disorders in a Memory Clinic Setting
| العنوان: | The Accuracy of Speech and Linguistic Analysis in Early Diagnostics of Neurocognitive Disorders in a Memory Clinic Setting |
|---|---|
| المؤلفون: | Daphne ter Huurne, Inez Ramakers, Nina Possemis, Leonie Banning, Angelique Gruters, Stephanie Van Asbroeck, Alexandra König, Nicklas Linz, Johannes Tröger, Kai Langel, Frans Verhey, Marjolein de Vugt |
| المصدر: | Archives of Clinical Neuropsychology. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | MILD COGNITIVE IMPAIRMENT, NORMATIVE DATA, EDUCATION, General Medicine, PERFORMANCE, ALZHEIMERS-DISEASE, Psychiatry and Mental health, Clinical Psychology, VERBAL FLUENCY, Neuropsychology and Physiological Psychology, AGE, Cognitive dysfunction, PARKINSONS-DISEASE, Neuropsychological tests, TESTS, Speech, PARTICIPANTS, Alzheimer disease |
| الوصف: | 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. |
| اللغة: | English |
| تدمد: | 0887-6177 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::449c9b2306b9d3201b0e91f334358209Test https://doi.org/10.1093/arclin/acac105Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....449c9b2306b9d3201b0e91f334358209 |
| قاعدة البيانات: | OpenAIRE |
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