Dementia Care Research and Psychosocial Factors

The clock drawing test (CDT) has been used as a cognitive screening tool for Alzheimer's disease (AD) and other forms of dementia. However, its clinical utility is constrained by requirements for trained scorers and non-standardized diagnostic criteria.

We developed a fully-automated vision transformer (ViT)-based dementia diagnostic pipeline incorporating convolutional neural network (CNN) preprocessing of hand drawn CDT images. The architecture implements fine-tuned ViT feature extraction followed by linear classification for dementia prediction. The method was trained using the National Health and Aging Trends Study (NHATS) dataset (n = 54027) and tested on an independent clinical cohort from the Toronto Dementia Research Alliance (TDRA) (n(dementia diagnosis) = 522, n(normal cognition) = 340).

The ViT-based approach demonstrated superior predictive performance (balanced accuracy = 76.5%) compared to both traditional human-scored CDT features (balanced accuracy = 74.3%) and three published deep learning architectures when evaluated on the TDRA dataset (balanced accuracy: MiniVGG = 73.3%, MNv2 = 72.3%, RF-VAE = 69.1%).

This pen-and-paper compatible, transformer-based diagnostic system enables scalable remote cognitive screening through automated CDT image analysis that is competitive with human-scored features, potentially increasing diagnostic accessibility and comfort for elderly populations across diverse socioeconomic contexts.