A novel vision transformer model produces clock drawing test scores as accurate as expert human coders.

ABSTRACT: Alzheimer's disease and related dementias is a growing public health concern. The clock-drawing test, where subjects draw a clock, typically with hands showing 11:10, has been widely used for dementia screening. A limitation of including the clock-drawing test in large-scale studies is that it requires manual coding, which could result in biases if coders interpret and implement coding rules differently. This study created and evaluated an intelligent Clock Scoring system built with deep learning neural networks to automatically code clock-drawing images. We used a large, publicly available repository of clock-drawing images from the 2011-2019 National Health and Aging Trends Study (NHATS) and compared three advanced DLNN methods - ResNet101, EfficientNet and Vision Transformers - in codin

SUBMITTER: Hu M 

PROVIDER: S-EPMC12856003 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature