A multimodal digital NeuroMarker platform with composite and domain-level validity for identification of mild cognitive impairment: a cross-sectional study

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.