A novel early screening approach for MCI due to AD based on a "maze" hand-interaction kinetic paradigm

Mild cognitive impairment due to Alzheimer's disease (MCI due to AD) is a crucial stage for the early identification of Alzheimer's disease (AD), and timely detection at this stage may provide opportunities for earlier intervention and potentially delay disease progression.

This study proposed a digital early-screening method based on a touchscreen maze hand-interaction kinetic paradigm, which integrates digital biomarkers from the visuospatial/executive and episodic memory domains to support the screening of MCI due to AD. A customized maze task was administered to 40 patients with clinically diagnosed MCI due to AD and 40 healthy controls (HC). Behavioral data were collected, and two categories of digital biomarkers were extracted: (1) visuospatial/executive digital biomarkers, such as task completion time (VSETT) and average movement speed (VSES); and (2) episodic memory digital biomarkers, such as episodic memory total time (EMTT) and number of correct choices (EMCC). Significant digital biomarkers were identified through between-group comparisons, and their combined classification performance was evaluated using binary logistic regression and receiver operating characteristic (ROC) analysis.

The integrated digital biomarker model showed promising apparent discriminative performance in the full cohort, with an AUC of 0.899 (95% CI: 0.831-0.967). To reduce potential optimism associated with biomarker selection, model development, and model evaluation within the same dataset, internal validation was performed using full-pipeline repeated stratified five-fold cross-validation with all 16 candidate digital biomarkers entered into the validation procedure and biomarker selection repeated within each training fold. The internally validated model retained good discriminative performance, with a mean cross-validated AUC of 0.842, an empirical 95% interval of 0.779-0.878, an accuracy of 0.783, a sensitivity of 0.772, and a specificity of 0.795.

These findings suggest that the proposed touchscreen maze-based digital assessment method may provide a promising and objective approach to supporting the early screening of MCI due to AD.