Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China.
Department of Medical Imaging, Shanxi Medical University, Taiyuan, China.
Sci Rep. 2024 Jan 10;14(1):982. doi: 10.1038/s41598-023-46710-y.
The population with dementia is expected to rise to 152 million in 2050 due to the aging population worldwide. Therefore, it is significant to identify and intervene in the early stage of dementia. The Rey-Osterreth complex figure (ROCF) test is a visuospatial test scale. Its scoring methods are numerous, time-consuming, and inconsistent, which is unsuitable for wide application as required by the high number of people at risk. Therefore, there is an urgent need for a rapid, objective, and sensitive digital scoring method to detect cognitive dysfunction in the early stage accurately. This study aims to clarify the organizational strategy of aMCI patients to draw complex figures through a multi-dimensional digital evaluation system. At the same time, a rapid, objective, and sensitive digital scoring method is established to replace traditional scoring. The data of 64 subjects (38 aMCI patients and 26 NC individuals) were analyzed in this study. All subjects completed the tablet's Geriatric Complex Figure (GCF) test, including copying, 3-min recall, and 20-min delayed recall, and also underwent a standardized neuropsychological test battery and classic ROCF test. Digital GCF (dGCF) variables and conventional GCF (cGCF) scores were input into the forward stepwise logistic regression model to construct classification models. Finally, ROC curves were made to visualize the difference in the diagnostic value of dGCF variables vs. cGCF scores in categorizing the diagnostic groups. In 20-min delayed recall, aMCI patients' time in air and pause time were longer than NC individuals. Patients with aMCI had more short strokes and poorer ability of detail integration (all p < 0.05). The diagnostic sensitivity of dGCF variables for aMCI patients was 89.47%, slightly higher than cGCF scores (sensitivity: 84.21%). The diagnostic accuracy of both was comparable (dGCF: 70.3%; cGCF: 73.4%). Moreover, combining dGCF variables and cGCF scores could significantly improve the diagnostic accuracy and specificity (accuracy: 78.1%, specificity: 84.62%). At the same time, we construct the regression equations of the two models. Our study shows that dGCF equipment can quantitatively evaluate drawing performance, and its performance is comparable to the time-consuming cGCF score. The regression equation of the model we constructed can well identify patients with aMCI in clinical application. We believe this new technique can be a highly effective screening tool for patients with MCI.
由于全球人口老龄化，预计到 2050 年，痴呆症患者人数将上升至 1.52 亿。因此，识别和干预痴呆症的早期阶段非常重要。 Rey-Osterreth 复杂图形（ROCF）测试是一种视觉空间测试量表。其评分方法众多、耗时且不一致，不适合广泛应用，因为高危人群数量众多。因此，迫切需要一种快速、客观、敏感的数字评分方法来准确检测早期认知功能障碍。本研究旨在通过多维数字评估系统阐明轻度认知障碍（aMCI）患者绘制复杂图形的组织策略。同时，建立一种快速、客观、敏感的数字评分方法来替代传统评分。本研究分析了 64 名受试者（38 名 aMCI 患者和 26 名 NC 个体）的数据。所有受试者均完成了平板电脑老年复杂图形（GCF）测试，包括临摹、3 分钟回忆和 20 分钟延迟回忆，还进行了标准化神经心理学测试和经典 ROCF 测试。将数字 GCF（dGCF）变量和常规 GCF（cGCF）评分输入向前逐步逻辑回归模型，构建分类模型。最后，制作 ROC 曲线，以可视化 dGCF 变量与 cGCF 评分在区分诊断组方面的诊断价值差异。在 20 分钟延迟回忆中，aMCI 患者的空中时间和暂停时间长于 NC 个体。患者的短笔画更多，细节整合能力更差（均 p<0.05）。dGCF 变量对 aMCI 患者的诊断敏感性为 89.47%，略高于 cGCF 评分（敏感性：84.21%）。两种方法的诊断准确性相当（dGCF：70.3%；cGCF：73.4%）。此外，结合 dGCF 变量和 cGCF 评分可以显著提高诊断准确性和特异性（准确性：78.1%，特异性：84.62%）。同时，我们构建了两个模型的回归方程。我们的研究表明，dGCF 设备可以定量评估绘图表现，其性能与耗时的 cGCF 评分相当。我们构建的模型的回归方程可以很好地识别临床应用中的 aMCI 患者。我们相信这项新技术可以成为 MCI 患者的一种高效筛查工具。
