Park Ingyu, Lee Sang-Kyu, Choi Hui-Chul, Ahn Moo-Eob, Ryu Ohk-Hyun, Jang Daehun, Lee Unjoo, Kim Yeo Jin
Department of Electronic Engineering, Hallym University, Chuncheon 24252, Republic of Korea.
Department of Psychiatry, Hallym University-Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea.
Brain Sci. 2024 May 9;14(5):480. doi: 10.3390/brainsci14050480.
In patients with mild cognitive impairment (MCI), a lower level of cognitive function is associated with a higher likelihood of progression to dementia. In addition, gait disturbances and structural changes on brain MRI scans reflect cognitive levels. Therefore, we aimed to classify MCI based on cognitive level using gait parameters and brain MRI data. Eighty patients diagnosed with MCI from three dementia centres in Gangwon-do, Korea, were recruited for this study. We defined MCI as a Clinical Dementia Rating global score of ≥0.5, with a memory domain score of ≥0.5. Patients were classified as early-stage or late-stage MCI based on their mini-mental status examination (MMSE) z-scores. We trained a machine learning model using gait and MRI data parameters. The convolutional neural network (CNN) resulted in the best classifier performance in separating late-stage MCI from early-stage MCI; its performance was maximised when feature patterns that included multimodal features (GAIT + white matter dataset) were used. The single support time was the strongest predictor. Machine learning that incorporated gait and white matter parameters achieved the highest accuracy in distinguishing between late-stage MCI and early-stage MCI.
在轻度认知障碍（MCI）患者中，较低的认知功能水平与发展为痴呆症的较高可能性相关。此外，步态障碍和脑部磁共振成像（MRI）扫描的结构变化反映了认知水平。因此，我们旨在使用步态参数和脑部MRI数据，根据认知水平对MCI进行分类。本研究招募了80名来自韩国江原道三个痴呆症中心、被诊断为MCI的患者。我们将MCI定义为临床痴呆评定量表（CDR）总分≥0.5，且记忆领域得分≥0.5。根据简易精神状态检查表（MMSE）的z分数，将患者分为早期或晚期MCI。我们使用步态和MRI数据参数训练了一个机器学习模型。卷积神经网络（CNN）在区分晚期MCI和早期MCI方面表现出最佳的分类器性能；当使用包含多模态特征（步态+白质数据集）的特征模式时，其性能达到最大化。单支撑时间是最强的预测指标。结合步态和白质参数的机器学习在区分晚期MCI和早期MCI方面具有最高的准确率。
