Handzlik Dakota, Richmond Lauren L, Skiena Steven, Carr Melissa A, Clouston Sean A P, Luft Benjamin J
Department of Computer Science Stony Brook University Stony Brook New York USA.
Department of Psychology Stony Brook University Stony Brook New York USA.
Alzheimers Dement (Amst). 2023 May 22;15(2):e12441. doi: 10.1002/dad2.12441. eCollection 2023 Apr-Jun.
The clock drawing task (CDT) is frequently used to aid in detecting cognitive impairment, but current scoring techniques are time-consuming and miss relevant features, justifying the creation of an automated quantitative scoring approach.
We used computer vision methods to analyze the stored scanned images ( = 7,109), and an intelligent system was created to examine these files in a study of aging World Trade Center responders. Outcomes were CDT, Montreal Cognitive Assessment (MoCA) score, and incidence of mild cognitive impairment (MCI).
The system accurately distinguished between previously scored CDTs in three CDT scoring categories: contour (accuracy = 92.2%), digits (accuracy = 89.1%), and clock hands (accuracy = 69.1%). The system reliably predicted MoCA score with CDT scores removed. Predictive analyses of the incidence of MCI at follow-up outperformed human-assigned CDT scores.
We created an automated scoring method using scanned and stored CDTs that provided additional information that might not be considered in human scoring.
画钟试验（CDT）常用于辅助检测认知障碍，但目前的评分技术耗时且会遗漏相关特征，因此有必要创建一种自动化定量评分方法。
我们使用计算机视觉方法分析存储的扫描图像（n = 7109），并创建了一个智能系统来检查这些文件，该研究针对在世贸中心事件中暴露的老年应急响应人员。结果指标包括画钟试验、蒙特利尔认知评估量表（MoCA）评分和轻度认知障碍（MCI）的发生率。
该系统能准确区分先前在三个画钟试验评分类别中评分的画钟试验：轮廓（准确率 = 92.2%）、数字（准确率 = 89.1%）和指针（准确率 = 69.1%）。该系统在去除画钟试验分数后能可靠地预测MoCA评分。对随访时轻度认知障碍发生率的预测分析优于人工分配的画钟试验分数。
我们使用扫描并存储的画钟试验创建了一种自动化评分方法，该方法提供了人工评分中可能未考虑的额外信息。
