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

> **NIH NIH R21** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $406,555

## Abstract

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 (CDT), 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 CDT 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 CDT 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 CDT 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 (DLNN),
a new and promising area of machine learning. More recently, machine learning methods have been applied to
digital CDT (dCDT), a form of CDT that uses a digital pen and tablet. Despite some promising results on small-
scale data, thus far dCDT studies have only attempted to code binary categories.
 The proposed study will develop advanced DLNN models to create and evaluate an intelligent CDT Clock
Scoring system – CloSco – that will automatically code CDT images. We will use a large, publicly available
repository of CDT 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 CDT-coding system for both ordinal and continuous scores; 2) Evaluate the
performance of the CloSco system and investigate the value of continuous CDT scoring for dementia
classification and longitudinal CDT models; and 3) Prepare and disseminate NHATS public-use files and
documentation with ordinal and continuous CDT codes assigned using CloSco along with the CloSco DLNN
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.

## Key facts

- **NIH application ID:** 10293176
- **Project number:** 1R21AG073971-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Mengyao Hu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $406,555
- **Award type:** 1
- **Project period:** 2021-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10293176

## Citation

> US National Institutes of Health, RePORTER application 10293176, Exploring the Use of Deep Learning Neural Networks to Improve Dementia Detection: Automating Coding of the Clock-Drawing Test (1R21AG073971-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10293176. Licensed CC0.

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