Mefford Joel A, Zhao Zilong, Heilier Leah, Xu Man, Zhou Guifeng, Mace Rachel, Sloane Kelly L, Sheppard Shannon M, Glenn Shenly
Department of Neurology, University of California, Los Angeles, California, United States of America.
Miro Health, Inc., San Francisco, California, United States of America.
PLOS Digit Health. 2023 Mar 13;2(3):e0000197. doi: 10.1371/journal.pdig.0000197. eCollection 2023 Mar.
A picture description task is a component of Miro Health's platform for self-administration of neurobehavioral assessments. Picture description has been used as a screening tool for identification of individuals with Alzheimer's disease and mild cognitive impairment (MCI), but currently requires in-person administration and scoring by someone with access to and familiarity with a scoring rubric. The Miro Health implementation allows broader use of this assessment through self-administration and automated processing, analysis, and scoring to deliver clinically useful quantifications of the users' speech production, vocal characteristics, and language. Picture description responses were collected from 62 healthy controls (HC), and 33 participants with MCI: 18 with amnestic MCI (aMCI) and 15 with non-amnestic MCI (naMCI). Speech and language features and contrasts between pairs of features were evaluated for differences in their distributions in the participant subgroups. Picture description features were selected and combined using penalized logistic regression to form risk scores for classification of HC versus MCI as well as HC versus specific MCI subtypes. A picture-description based risk score distinguishes MCI and HC with an area under the receiver operator curve (AUROC) of 0.74. When contrasting specific subtypes of MCI and HC, the classifiers have an AUROC of 0.88 for aMCI versus HC and and AUROC of 0.61 for naMCI versus HC. Tests of association of individual features or contrasts of pairs of features with HC versus aMCI identified 20 features with p-values below 5e-3 and False Discovery Rates (FDRs) at or below 0.113, and 61 contrasts with p-values below 5e-4 and FDRs at or below 0.132. Findings suggest that performance of picture description as a screening tool for MCI detection will vary greatly by MCI subtype or by the proportion of various subtypes in an undifferentiated MCI population.
图片描述任务是Miro Health公司用于神经行为评估自我管理平台的一个组成部分。图片描述已被用作识别阿尔茨海默病和轻度认知障碍（MCI）患者的筛查工具，但目前需要由能够获取并熟悉评分标准的人员进行现场施测和评分。Miro Health公司的实施方案通过自我管理以及自动化处理、分析和评分，使该评估能够得到更广泛的应用，从而提供对用户言语表达、语音特征和语言的临床有用量化指标。从62名健康对照者（HC）和33名MCI参与者中收集了图片描述反应：18名遗忘型MCI（aMCI）患者和15名非遗忘型MCI（naMCI）患者。评估了言语和语言特征以及特征对之间的差异，以了解它们在参与者亚组中的分布差异。使用惩罚逻辑回归选择并组合图片描述特征，以形成用于区分HC与MCI以及HC与特定MCI亚型的风险评分。基于图片描述的风险评分在受试者工作特征曲线（AUROC）下的面积为0.74，可区分MCI和HC。在对比MCI和HC的特定亚型时，分类器对aMCI与HC的AUROC为0.88，对naMCI与HC的AUROC为0.61。对个体特征或特征对与HC对比aMCI的关联测试确定了20个p值低于5e-3且错误发现率（FDR）等于或低于0.113的特征，以及61个p值低于5e-4且FDR等于或低于0.132的对比。研究结果表明，图片描述作为MCI检测筛查工具的性能，会因MCI亚型或未分化MCI人群中各种亚型的比例而有很大差异。
