Validation of the Remote Automated ki:e Speech Biomarker for Cognition in Mild Cognitive Impairment: Verification and Validation following DiME V3 Framework

Abstract

<b><i>Introduction:</i></b> 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. <b><i>Methods:</i></b> Evaluation was done in two independent clinical samples: the Dutch DeepSpA (<i>N</i> = 69 subjective cognitive impairment [SCI], <i>N</i> = 52 mild cognitive impairment [MCI], and <i>N</i> = 13 dementia) and the Scottish SPeAk datasets (<i>N</i> = 25, healthy controls). For validation, two anchor scores were used: the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) scale. <b><i>Results:</i></b> <i>Verification</i>: The SB-C could be reliably extracted for both languages using an automatic speech processing pipeline. <i>Analytical Validation</i>: In both languages, the SB-C was strongly correlated with MMSE scores. <i>Clinical Validation:</i> 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. <b><i>Conclusion:</i></b> 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.

Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment

“…However, these open-ended approaches are subjected to more individual and contextual factors, resulting in more variability and limiting their generalizability when contrasting results between studies (Mueller et al, 2018 ). More efforts should be devoted to providing standards and protocols to improve the accuracy of procedures and algorithms and to stimulate the integration of innovative solutions in SS processing to clinical practice or trials (Haider et al, 2019 ; Tröger et al, 2022 ). In this sense, it should be noted that one of the strengths of the present study was to provide precise discriminant results by distinguishing the amyloid status (positive vs negative) in a relevant clinical population (patients with MCI) in an applied setting (a memory unit), using a simple speech strategy (description of a picture), through a very well-known and accessible tool (the Cookie Theft picture), administered during less than 1 min, and focusing attention only on acoustic features, obtained using a set of standardized variables (eGeMAPS) (Eyben et al, 2015 ).…”

“…These findings, coupled with the digitalization experienced over the last few years, have increased the popularity of spontaneous speech (SS) protocols administered using digital tools (Beltrami et al, 2018 ; de la Fuente Garcia et al, 2019 ; Thomas et al, 2020 ). Classical cognitive assessments, based on traditional settings (i.e., neuropsychological batteries) usually demand the physical presence of clinicians in specialized health centers and are not always optimal for decentralized remote clinical trials (Tröger et al, 2022 ). In contrast, digital cognitive assessments are better-suited protocols when automated procedures are recommended or needed (Lindsay et al, 2021 ).…”

Remote data collection speech analysis in people at risk for Alzheimer's disease dementia: usability and acceptability results

“…An iPad with the Mili platform (previously called the Delta Testing App) was used to place the phone calls to participants and facilitate the assessments. The Mili platform has been validated in a previous study for the semantic fluency task (Tröger et al, 2018(Tröger et al, , 2019 and the speech biomarker for cognition (Tröger et al, 2022). The first visit was completed with a trained research assistant delivering verbatim task instructions, while the second visit used an automated voice to provide the instructions.…”

Feature Importance Analysis and Machine Learning for Alzheimer’s Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio

“…However, digital biomarkers, such as speech characteristics, offer non-invasive monitoring and detection of AD. Digital biomarkers have the potential for continual, non-invasive monitoring, potentially enabling early detection and longitudinal tracking of cognitive decline [ 10 ]. While we focus on using measurements taken from imaging modalities due to the wealth of the available datasets and their high accuracy, we do so without overshadowing the emerging importance of recent digital biomarkers.…”