Exploring the Use of Deep Learning Neural Networks to Improve Dementia Detection: Automating Coding of the Clock-Drawing Test

Data & Methods, Life Course

Project Summary

Alzheimer’s disease and related dementias (ADRD), a leading cause of disability among older adults, has become a critical public health concern. The clock-drawing test, which measures multiple aspects of cognitive function including comprehension, visual spatial abilities, executive function and memory, has been widely used as a screening tool to detect dementia in clinical research, epidemiologic studies, and panel surveys. The test asks subjects to draw a clock, typically with hands showing ten after 11, and then assigns either a binary (e.g. normal vs. abnormal) or ordinal (e.g. 0 to 5) score. An important limitation in large-scale studies is that the test requires manual coding, which could result in biases if coders interpret and implement coding rules in different ways.

Several small-scale studies have explored the use of machine learning methods to automate clock-drawing test coding. Such studies, which have had limited success with ordinal coding, have used methods that are not designed specifically for complex image classification and are less effective than deep learning neural networks, a new and promising area of machine learning. More recently, machine-learning methods have been applied to digital clock-drawing testing, a form of the clock-drawing test that uses a digital pen and tablet. Despite some promising results on small-scale data, thus far these studies have only attempted to code binary categories.

This project develops advanced deep learning neural network models to create and evaluate an intelligent clock-drawing test Clock Scoring system – CloSco – that will automatically code test images. We will use a large, publicly available repository of clock-drawing test images from the 2011-2019 National Health and Aging Trends Study (NHATS), a panel study of Medicare beneficiaries ages 65 and older funded by the National Institute on Aging. Specifically, we will:

  1. Develop an automated clock-drawing test coding system for both ordinal and continuous scores;
  2. Evaluate the performance of the CloSco system and investigate the value of continuous scoring for dementia classification and longitudinal test models; and
  3. Prepare and disseminate NHATS public-use files and documentation with ordinal and continuous clock-drawing test codes assigned using CloSco along with the CloSco deep learning neural network program.

If successful, the DLNN programs may offer a model for automating coding of other widely available drawing tests used to evaluate a variety of cognitive functions.


Mengyao Hu

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