神经认知
波士顿命名测验
认知
听力学
神经心理评估
痴呆
逻辑回归
接收机工作特性
神经心理学
认知测验
心理学
医学
疾病
精神科
内科学
作者
Zampeta-Sofia Alexopoulou,Stefanie Köhler,Elisa Mallick,J. Tröger,Nicklas Linz,Eike Spruth,Klaus Fließbach,Claudia Bartels,Ayda Rostamzadeh,Wenzel Glanz,Enise I. Incesoy,Michaela Butryn,Ingo Kilimann,Sebastian Sodenkamp,Matthias H. Munk,Antje Osterrath,A F Esser,Sandra Roeske,Ingo Frommann,Melina Stark
标识
DOI:10.1177/13872877251343296
摘要
Background Cognitive decline in Alzheimer's disease (AD) often includes speech impairments, where subtle changes may precede clinical dementia onset. As clinical trials focus on early identification of patients for disease-modifying treatments, digital speech-based assessments for scalable screening have become crucial. Objective This study aimed to validate a remote, speech-based digital cognitive assessment for mild cognitive impairment (MCI) detection through the comparison with gold-standard paper-based neurocognitive assessments. Methods Within the PROSPECT-AD project, speech and clinical data were obtained from the German DELCODE and DESCRIBE cohorts, including 21 healthy controls (HC), 110 participants with subjective cognitive decline (SCD), and 59 with MCI. Spearman rank and partial correlations were computed between speech-based scores and clinical measures. Kruskal-Wallis tests assessed group differences. We trained machine learning models to classify diagnostic groups comparing classification accuracies between gold-standard assessment scores and a speech-based digital cognitive assessment composite score (SB-C). Results Global cognition, as measured by SB-C, significantly differed between diagnostic groups ( H (2) = 30.93, p < 0.001). Speech-based scores were significantly correlated with global anchor scores (MMSE, CDR, PACC5). Speech-based composites for memory, executive function and processing speed were also correlated with respective domain-specific paper-based assessments. In logistic regression classification, the model combining SB-C and neuropsychological tests at baseline achieved a high discriminatory power in differentiating HC/SCD from MCI patients (Area Under the Curve = 0.86). Conclusions Our findings support speech-based cognitive assessments as a promising avenue towards remote MCI screening, with implications for scalable screening in clinical trials and healthcare.
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