Speech-based digital cognitive assessments for detection of mild cognitive impairment: Validation against paper-based neurocognitive assessment scores

Abstract

BackgroundCognitive 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.ObjectiveThis 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.MethodsWithin 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).ResultsGlobal 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).ConclusionsOur findings support speech-based cognitive assessments as a promising avenue towards remote MCI screening, with implications for scalable screening in clinical trials and healthcare.

Keywords: Alzheimer's disease; dementia; digital cognitive assessment; mild cognitive impairment; speech analysis; speech-based digital cognitive assessment; speech-based markers.

Figure 1.

Overview of the design of the DELCODE and DESCRIBE cohorts and the PROSPECT-AD study.

Figure 2.

Overview of the structure of the speech-based digital cognitive assessment composite score SB-C and its domain-specific composite scores. Adapted from Tröger et al..

Figure 3.

(a) Group differences in the SB-C global cognition score between diagnostic groups. (b) Group differences in the SB-C global cognition score according to global CDR scores.

Figure 4.

Correlations between the SB-C global cognition score and (a) MMSE total score, (b). CDR SoB score, and (c) PACC5 total score. The red line indicates the fitted smoothed regression line and the shaded area represents its confidence intervals.

Figure 5.

Receiver operator curves for differentiating HC/SCD and MCI patients. The MMSE total score at T0 is displayed in a yellow line. The ADAS-Cog 13 Sum score at T0 is displayed in a purple line. The SB-C global cognition score at M0 is displayed in a green line. The aggregated SB-C global cognition score of M0, M3 and M6 is displayed in a pink line. The model combining MMSE and ADAS-Cog 13 Sum score at T0 and the SB-C global cognition score at M0 is displayed in a red line. ROC-AUC: Receiver Operator Characteristic-Area Under the Curve; MMSE: Mini-Mental State Examination; ADAS-Cog 13 Sum: Alzheimer's Disease Assessment Scale 13-item Cognitive Subscale Sum score. MCI: mild cognitive impairment; SCD: subjective cognitive decline.