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

• 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
• 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' s disease related dementia; Alzheimer' 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

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.

项目概要 阿尔茨海默氏病和相关痴呆症 (ADRD) 是导致老年人残疾的主要原因 成为一个重要的公共卫生问题。时钟绘图测试（CDT），测量多个方面 认知功能包括理解力、视觉空间能力、执行功能和记忆力 在临床研究、流行病学研究和小组中广泛用作检测痴呆症的筛查工具 调查。 CDT 要求受试者画一个时钟，通常指针在 11 后显示 10，然后分配 二进制（例如正常与异常）或序数（例如 0 到 5）分数。大规模应用的一个重要限制 研究表明 CDT 需要手动编码，如果编码者解释和实施，可能会导致偏差 不同方式的编码规则。 一些小规模的研究探索了使用机器学习方法来自动化 CDT 编码。 此类研究在序数编码方面取得了有限的成功，并且使用了未设计的方法 专门针对复杂图像分类，其效率不如深度学习神经网络（DLNN）， 机器学习的一个新的、有前途的领域。最近，机器学习方法已被应用于 数字 CDT (dCDT)，一种使用数字笔和平板电脑的 CDT 形式。尽管在小型研究中取得了一些有希望的结果 规模数据，迄今为止 dCDT 研究仅尝试对二进制类别进行编码。 拟议的研究将开发先进的 DLNN 模型来创建和评估智能 CDT 时钟 评分系统 – CloSco – 将自动对 CDT 图像进行编码。我们将使用一个大型的、公开的 2011-2019 年国家健康和老龄化趋势研究 (NHATS) 小组的 CDT 图像存储库 由国家老龄化研究所资助的针对 65 岁及以上医疗保险受益人的研究。具体来说，我们 将： 1) 开发一个用于序数和连续分数的自动 CDT 编码系统； 2) 评估 CloSco 系统的性能并研究连续 CDT 评分对痴呆症的价值 分类和纵向CDT模型； 3) 准备并传播 NHATS 公共使用文件 使用 CloSco 和 CloSco DLNN 分配序数和连续 CDT 代码的文档 程序。如果成功，DLNN 程序可能会提供一个模型，用于自动编码其他广泛可用的 用于评估各种认知功能的绘图测试。