Ahn Kichan, Cho Minwoo, Kim Suk Wha, Lee Kyu Eun, Song Yoojin, Yoo Seok, Jeon So Yeon, Kim Jeong Lan, Yoon Dae Hyun, Kong Hyoun-Joong
Interdisciplinary Program in Medical Informatics Major, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea.
Bioengineering (Basel). 2023 Sep 18;10(9):1093. doi: 10.3390/bioengineering10091093.
Alzheimer's disease (AD) is the most common form of dementia, which makes the lives of patients and their families difficult for various reasons. Therefore, early detection of AD is crucial to alleviating the symptoms through medication and treatment.
Given that AD strongly induces language disorders, this study aims to detect AD rapidly by analyzing the language characteristics.
The mini-mental state examination for dementia screening (MMSE-DS), which is most commonly used in South Korean public health centers, is used to obtain negative answers based on the questionnaire. Among the acquired voices, significant questionnaires and answers are selected and converted into mel-frequency cepstral coefficient (MFCC)-based spectrogram images. After accumulating the significant answers, validated data augmentation was achieved using the Densenet121 model. Five deep learning models, Inception v3, VGG19, Xception, Resnet50, and Densenet121, were used to train and confirm the results.
Considering the amount of data, the results of the five-fold cross-validation are more significant than those of the hold-out method. Densenet121 exhibits a sensitivity of 0.9550, a specificity of 0.8333, and an accuracy of 0.9000 in a five-fold cross-validation to separate AD patients from the control group.
The potential for remote health care can be increased by simplifying the AD screening process. Furthermore, by facilitating remote health care, the proposed method can enhance the accessibility of AD screening and increase the rate of early AD detection.
阿尔茨海默病（AD）是最常见的痴呆形式，由于各种原因给患者及其家人的生活带来困难。因此，早期发现AD对于通过药物治疗缓解症状至关重要。
鉴于AD强烈诱发语言障碍，本研究旨在通过分析语言特征快速检测AD。
韩国公共卫生中心最常用的用于痴呆筛查的简易精神状态检查表（MMSE-DS）用于根据问卷获取否定答案。在获取的语音中，选择重要的问卷和答案并转换为基于梅尔频率倒谱系数（MFCC）的频谱图图像。在积累重要答案后，使用Densenet121模型实现了经过验证的数据增强。使用Inception v3、VGG19、Xception、Resnet50和Densenet121这五个深度学习模型进行训练并确认结果。
考虑到数据量，五折交叉验证的结果比留出法的结果更显著。在将AD患者与对照组分开的五折交叉验证中，Densenet121的灵敏度为0.9550，特异性为0.8333，准确率为0.9000。
简化AD筛查过程可以增加远程医疗保健的潜力。此外，通过促进远程医疗保健，所提出的方法可以提高AD筛查的可及性并提高AD早期检测率。
