A multimodal digital NeuroMarker platform with composite and domain-level validity for identification of mild cognitive impairment: a cross-sectional study
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Updated: Nov 2, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
M Florencia Iulita1, Vijaya B Kolachalama2,3, Victoria Brugada-Ramentol1
1Altoida Inc., Washington, DC, USA.
Background:
Mild cognitive impairment (MCI) is a clinically heterogeneous syndrome that can represent an early manifestation of neurodegenerative disease. Diagnosis relies on assessments that are difficult to scale, leaving many individuals unidentified.
Methods:
In this cross-sectional study conducted in the United States, Australia, and Spain between May 2022 and October 2024, 638 adults aged ≥ 50 years were classified as cognitively unimpaired (CU; n = 441) or MCI (n = 197) using Petersen/NIA-AA criteria. Participants completed a self-administered, tablet-based assessment (Altoida NeuroMarker Platform) integrating motor, speech, and augmented reality tasks. An XGBoost classifier was trained to distinguish CU from MCI. Diagnostic performance, test-retest reliability, and convergent validity were evaluated.
Findings:
Median assessment time was 13.8 min. Participants reported comfort with digital technology (67%), willingness to engage in cognitive screening (90%), and positive usability. Altoida's NeuroMarker differentiated MCI with ROC-AUC 0.90, 95% CI 0.88-0.92 and comparable performance in an independent cohort (ROC-AUC 0.87, 95% CI 0.80-0.93; n = 75). Cognitive composite and domain scores were lower in MCI than in CU. Classifier agreement across repeated assessments was κ = 0.60, and reliability of composite and domain scores was moderate-to-good (ICC 0.55-0.80). The composite score correlated with the Mini-Mental State Examination (MMSE) (r s = 0.40, p < 0.001) and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) Total Index (r s = 0.58, p < 0.001). The platform also showed convergent validity of cognitive domains (|r s| = 0.27-0.50, p < 0.001).
Interpretation:
A brief, self-administered multimodal assessment identified clinically adjudicated MCI and demonstrated convergent validity, test-retest reliability, and consistent performance in an independent cohort. Prospective studies should evaluate its implementation in real-world workflows.
Funding:
Alzheimer's Drug Discovery Foundation; Altoida Inc.
