-
1دورية أكاديمية
المؤلفون: Daphne ter Huurne, Nina Possemis, Leonie Banning, Angélique Gruters, Alexandra König, Nicklas Linz, Johannes Tröger, Kai Langel, Frans Verhey, Marjolein de Vugt, Inez Ramakers
المصدر: Digital Biomarkers, Vol 7, Iss 1, Pp 115-123 (2023)
مصطلحات موضوعية: phone assessment, automated speech analysis, mild cognitive impairment, memory, fluency, Biology (General), QH301-705.5
الوصف: Introduction: We studied the accuracy of the automatic speech recognition (ASR) software by comparing ASR scores with manual scores from a verbal learning test (VLT) and a semantic verbal fluency (SVF) task in a semiautomated phone assessment in a memory clinic population. Furthermore, we examined the differentiating value of these tests between participants with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). We also investigated whether the automatically calculated speech and linguistic features had an additional value compared to the commonly used total scores in a semiautomated phone assessment. Methods: We included 94 participants from the memory clinic of the Maastricht University Medical Center+ (SCD N = 56 and MCI N = 38). The test leader guided the participant through a semiautomated phone assessment. The VLT and SVF were audio recorded and processed via a mobile application. The recall count and speech and linguistic features were automatically extracted. The diagnostic groups were classified by training machine learning classifiers to differentiate SCD and MCI participants. Results: The intraclass correlation for inter-rater reliability between the manual and the ASR total word count was 0.89 (95% CI 0.09–0.97) for the VLT immediate recall, 0.94 (95% CI 0.68–0.98) for the VLT delayed recall, and 0.93 (95% CI 0.56–0.97) for the SVF. The full model including the total word count and speech and linguistic features had an area under the curve of 0.81 and 0.77 for the VLT immediate and delayed recall, respectively, and 0.61 for the SVF. Conclusion: There was a high agreement between the ASR and manual scores, keeping the broad confidence intervals in mind. The phone-based VLT was able to differentiate between SCD and MCI and can have opportunities for clinical trial screening.
وصف الملف: electronic resource
-
2دورية 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
النص الكامل