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

探索使用深度学习神经网络来改进痴呆症检测：自动绘制时钟测试编码

基本信息

批准号：
10293176
负责人：
Mengyao Hu
金额：
$ 40.66万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10293176
关键词：
Affect Age Aging Algorithms Alzheimer&apos s disease related dementia Alzheimer&apos s disease test Area Categories Classification Clinical Research Code Cognition Comparative Study Complex Comprehension Consumption Data Dementia Detection Development Documentation Elderly Elements Error Sources Evaluation Family health status Funding Hand Health Healthcare Systems Human Image Impaired cognition Individual Intelligence Lead Longitudinal Studies Machine Learning Manuals Measures Medicare Memory Memory impairment Methodology Methods Modeling National Institute on Aging Neural Network Simulation Paper Performance Population Public Health Research Research Personnel Research Support Respondent Sampling Screening procedure Support System Surveys System Tablets Techniques Testing Time Training Validity and Reliability Visuospatial Writing artificial neural network base beneficiary cognitive function deep learning deep neural network design digital disability epidemiology study executive function human old age (65+)improved learning strategy longitudinal analysis machine learning method mild cognitive impairment performance tests programs public repository repository screening success tool trend

项目摘要

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.

项目总结