JMIR Publications

ABSTRACT

Background:

With the aging of populations worldwide, early detection of cognitive impairments has become a research and clinical priority, particularly to enable preventive intervention for dementia. Automated drawing analysis has been studied as a promising means for lightweight, self-administered cognitive assessment. However, this approach has not been sufficiently tested for its generalizability across populations.

Objective:

The aim of this study was to evaluate the generalizability of automated drawing analysis for predicting global cognition in community-dwelling older adults across populations in different nations.

Methods:

We collected drawing data with a digital tablet, along with Montreal Cognitive Assessment (MoCA) scores for assessment of global cognition, from 92 community-dwelling older adults in the USA and Japan. We automatically extracted six drawing features that characterize the drawing process in terms of the drawing speed, pauses between drawings, pen pressure, and pen inclinations We then investigated the association between the drawing features and MoCA scores through correlation and machine-learning-based regression analyses.

Results:

We found that with low MoCA scores, there tended to be higher variability in the drawing speed, a higher pause/drawing duration ratio, and lower variability in the pen’s horizontal inclination in both the US and Japan datasets. A machine-learning model that used drawing features to estimate MoCA scores demonstrated its capability to generalize from the US dataset to the Japan dataset (R2 = 0.35; permutation test, P < .001).

Conclusions:

This study presents initial empirical evidence of the capability of automated drawing analysis as a predictor of global cognition that is generalizable across populations. Our results suggest that such automated drawing analysis may enable the development of a practical tool for international use in self-administered, automated cognitive assessment.