Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands.
National Institute for Research in Computer Science and Automation (INRIA), Stars Team, Sophia Antipolis, France.
Arch Clin Neuropsychol. 2023 Jul 25;38(5):667-676. doi: 10.1093/arclin/acac105.
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.
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.
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.
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.
研究语义流畅性测试（SVF）的自动分析是否可靠，以及是否可以提取有价值的额外信息来识别神经认知障碍。此外，还探讨了自动提取的言语和语言特征与其他认知领域之间的关联。
我们纳入了来自马斯特里赫特大学医学中心记忆诊所的 135 名参与者（有主观认知减退[SCD；N=69]和轻度认知障碍[MCI]/痴呆[N=66]）。SVF 任务（一分钟，动物类别）通过移动应用程序记录和处理，并自动提取言语和语言特征。通过训练机器学习分类器来区分 SCD 和 MCI/痴呆参与者，来研究自动提取特征的诊断性能。
临床总分（金标准）和自动提取的总单词数之间的组内相关系数为 0.84。包括总单词数和自动提取的言语和语言特征的全模型在区分 SCD 和 MCI/痴呆患者方面的 AUC 为 0.85。仅包含总单词数的模型和校正年龄后的总单词数模型的 AUC 分别为 0.75 和 0.81。语义转换与记忆以及执行功能中度相关。
具有自动提取的言语和语言特征的一分钟 SVF 任务与手动评分一样可靠，可以很好地区分 SCD 和 MCI/痴呆。这可以被认为是神经认知障碍筛查和临床实践中的一个有价值的补充。
