Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Department of Computer Science and Technolgy, College of Electronic and Information Engineering, Tongji University, Shanghai, China.
J Med Internet Res. 2023 Jun 9;25:e42637. doi: 10.2196/42637.
Computer-aided detection, used in the screening and diagnosing of cognitive impairment, provides an objective, valid, and convenient assessment. Particularly, digital sensor technology is a promising detection method.
This study aimed to develop and validate a novel Trail Making Test (TMT) using a combination of paper and electronic devices.
This study included community-dwelling older adult individuals (n=297), who were classified into (1) cognitively healthy controls (HC; n=100 participants), (2) participants diagnosed with mild cognitive impairment (MCI; n=98 participants), and (3) participants with Alzheimer disease (AD; n=99 participants). An electromagnetic tablet was used to record each participant's hand-drawn stroke. A sheet of A4 paper was placed on top of the tablet to maintain the traditional interaction style for participants who were not familiar or comfortable with electronic devices (such as touchscreens). In this way, all participants were instructed to perform the TMT-square and circle. Furthermore, we developed an efficient and interpretable cognitive impairment-screening model to automatically analyze cognitive impairment levels that were dependent on demographic characteristics and time-, pressure-, jerk-, and template-related features. Among these features, novel template-based features were based on a vector quantization algorithm. First, the model identified a candidate trajectory as the standard answer (template) from the HC group. The distance between the recorded trajectories and reference was computed as an important evaluation index. To verify the effectiveness of our method, we compared the performance of a well-trained machine learning model using the extracted evaluation index with conventional demographic characteristics and time-related features. The well-trained model was validated using follow-up data (HC group: n=38; MCI group: n=32; and AD group: n=22).
We compared 5 candidate machine learning methods and selected random forest as the ideal model with the best performance (accuracy: 0.726 for HC vs MCI, 0.929 for HC vs AD, and 0.815 for AD vs MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method, with high stability and accuracy of the follow-up data.
The study demonstrated that a model combining both paper and electronic TMTs increases the accuracy of evaluating participants' cognitive impairment compared to conventional paper-based feature assessment.
计算机辅助检测用于认知障碍的筛查和诊断，提供了客观、有效和方便的评估。特别是，数字传感器技术是一种很有前途的检测方法。
本研究旨在开发和验证一种使用纸质和电子设备相结合的新型连线测试（TMT）。
本研究纳入了社区居住的老年个体（n=297），分为（1）认知健康对照组（HC；n=100 名参与者）、（2）轻度认知障碍（MCI；n=98 名参与者）和（3）阿尔茨海默病（AD；n=99 名参与者）。使用电磁平板记录每个参与者的手绘笔触。在平板上放置一张 A4 纸，以保持对不熟悉或不适应电子设备（如触摸屏）的参与者的传统交互方式。这样，所有参与者都被指示执行 TMT 正方形和圆形。此外，我们开发了一种高效且可解释的认知障碍筛查模型，该模型可自动分析依赖于人口统计学特征以及时间、压力、冲击和模板相关特征的认知障碍水平。在这些特征中，新颖的基于模板的特征基于矢量量化算法。首先，模型从 HC 组中识别候选轨迹作为标准答案（模板）。计算记录轨迹与参考轨迹之间的距离作为重要的评估指标。为了验证我们方法的有效性，我们将使用提取的评估指标训练有素的机器学习模型的性能与传统的人口统计学特征和时间相关特征进行了比较。使用后续数据（HC 组：n=38；MCI 组：n=32；AD 组：n=22）对训练有素的模型进行了验证。
我们比较了 5 种候选机器学习方法，选择随机森林作为性能最佳的理想模型（HC 与 MCI 比较的准确率：0.726，HC 与 AD 比较的准确率：0.929，AD 与 MCI 比较的准确率：0.815）。同时，训练有素的分类器的表现优于传统评估方法，具有后续数据的高稳定性和准确性。
该研究表明，与传统的基于纸质的特征评估相比，结合纸质和电子 TMT 的模型可提高评估参与者认知障碍的准确性。
