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1دورية أكاديمية
المؤلفون: Johannes Tröger, Ebru Baykara, Jian Zhao, Daphne ter Huurne, Nina Possemis, Elisa Mallick, Simona Schäfer, Louisa Schwed, Mario Mina, Nicklas Linz, Inez Ramakers, Craig Ritchie
المصدر: Digital Biomarkers, Vol 6, Iss 3, Pp 107-116 (2022)
مصطلحات موضوعية: mild cognitive impairment, digital biomarker, speech biomarker, dementia, speech analysis, clinical trials, Biology (General), QH301-705.5
الوصف: Introduction: Progressive cognitive decline is the cardinal behavioral symptom in most dementia-causing diseases such as Alzheimer’s disease. While most well-established measures for cognition might not fit tomorrow’s decentralized remote clinical trials, digital cognitive assessments will gain importance. We present the evaluation of a novel digital speech biomarker for cognition (SB-C) following the Digital Medicine Society’s V3 framework: verification, analytical validation, and clinical validation. Methods: Evaluation was done in two independent clinical samples: the Dutch DeepSpA (N = 69 subjective cognitive impairment [SCI], N = 52 mild cognitive impairment [MCI], and N = 13 dementia) and the Scottish SPeAk datasets (N = 25, healthy controls). For validation, two anchor scores were used: the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) scale. Results: Verification: The SB-C could be reliably extracted for both languages using an automatic speech processing pipeline. Analytical Validation: In both languages, the SB-C was strongly correlated with MMSE scores. Clinical Validation: The SB-C significantly differed between clinical groups (including MCI and dementia), was strongly correlated with the CDR, and could track the clinically meaningful decline. Conclusion: Our results suggest that the ki:e SB-C is an objective, scalable, and reliable indicator of cognitive decline, fit for purpose as a remote assessment in clinical early dementia trials.
وصف الملف: electronic resource
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المؤلفون: Simona, Schäfer, Elisa, Mallick, Louisa, Schwed, Alexandra, König, Jian, Zhao, Nicklas, Linz, Timothy Hadarsson, Bodin, Johan, Skoog, Nina, Possemis, Daphne, Ter Huurne, Anna, Zettergren, Silke, Kern, Simona, Sacuiu, Inez, Ramakers, Ingmar, Skoog, Johannes, Tröger
المساهمون: RS: MHeNs - R3 - Neuroscience, Basic Neuroscience 2, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, Psychology 1, Psychology 2
المصدر: Journal of Alzheimer's Disease, 91(3), 1165-1171. IOS Press
مصطلحات موضوعية: Psychiatry and Mental health, Clinical Psychology, General Neuroscience, General Medicine, Geriatrics and Gerontology
الوصف: BACKGROUND: Modern prodromal Alzheimer's disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed.OBJECTIVE: Develop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations.METHODS: Two screening algorithms based on the remote ki:e speech biomarker for cognition (ki:e SB-C) were designed on a Dutch memory clinic cohort (N = 121) and a Swedish birth cohort (N = 404). MCI classification was each evaluated on the training cohort as well as across on the unrelated validation cohort.RESULTS: The algorithms achieved a performance of AUC 0.73 and AUC 0.77 in the respective training cohorts and AUC 0.81 in the unseen validation cohort.CONCLUSION: The results indicate that a ki:e SB-C based algorithm robustly detects MCI across different cohorts and languages, which has the potential to make current trials more efficient and improve future primary health care.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::309dee186fd0b3643ecb8375d6ea4d0fTest
https://doi.org/10.3233/jad-220762Test
النص الكامل