Souillard-Mandar William, Penney Dana, Schaible Braydon, Pascual-Leone Alvaro, Au Rhoda, Davis Randall
Digital Cognition Technologies, Boston, MA, United States.
Linus Health, Boston, MA, United States.
Front Digit Health. 2021 Oct 15;3:750661. doi: 10.3389/fdgth.2021.750661. eCollection 2021.
Developing tools for efficiently measuring cognitive change specifically and brain health generally-whether for clinical use or as endpoints in clinical trials-is a major challenge, particularly for conditions such as Alzheimer's disease. Technology such as connected devices and advances in artificial intelligence offer the possibility of creating and deploying clinical-grade tools with high sensitivity, rapidly, cheaply, and non-intrusively. Starting from a widely-used paper and pencil cognitive status test-The Clock Drawing Test-we combined a digital input device to capture time-stamped drawing coordinates with a machine learning analysis of drawing behavior to create DCTclock™, an automated analysis of nuances in cognitive performance beyond successful task completion. Development and validation was conducted on a dataset of 1,833 presumed cognitively unimpaired and clinically diagnosed cognitively impaired individuals with varied neurological conditions. We benchmarked DCTclock against existing clock scoring systems and the Mini-Mental Status Examination, a widely-used but lengthier cognitive test, and showed that DCTclock offered a significant improvement in the detection of early cognitive impairment and the ability to characterize individuals along the Alzheimer's disease trajectory. This offers an example of a robust framework for creating digital biomarkers that can be used clinically and in research for assessing neurological function.
开发专门用于有效测量认知变化以及总体脑健康的工具——无论是用于临床还是作为临床试验的终点——都是一项重大挑战，尤其是对于阿尔茨海默病等病症而言。诸如联网设备之类的技术以及人工智能的进步，为快速、廉价且非侵入性地创建和部署具有高灵敏度的临床级工具提供了可能性。从一种广泛使用的纸笔认知状态测试——画钟试验——开始，我们将一个用于捕捉带时间戳绘图坐标的数字输入设备与对绘图行为的机器学习分析相结合，创建了DCTclock™，这是一种对认知表现细微差别进行的自动化分析，超越了任务成功完成的范畴。我们在一个包含1833名假定认知未受损以及临床诊断为认知受损且患有不同神经病症的个体的数据集上进行了开发和验证。我们将DCTclock与现有的画钟评分系统以及简易精神状态检查表（一种广泛使用但耗时较长的认知测试）进行了对比，结果表明DCTclock在早期认知障碍检测以及沿着阿尔茨海默病轨迹对个体进行特征描述的能力方面有显著提升。这为创建可用于临床和研究以评估神经功能的数字生物标志物提供了一个强大框架的示例。
