Early detection of Mild Cognitive Impairment (MCI) leads to early
interventions to slow the progression from MCI into dementia. Deep Learning
(DL) algorithms could help achieve early non-invasive, low-cost detection of
MCI. This paper presents the detection of MCI in older adults using DL models
based only on facial features extracted from video-recorded conversations at
home. We used the data collected from the I-CONECT behavioral intervention
study (NCT02871921), where several sessions of semi-structured interviews
between socially isolated older individuals and interviewers were video
recorded. We develop a framework that extracts spatial holistic facial features
using a convolutional autoencoder and temporal information using transformers.
Our proposed DL model was able to detect the I-CONECT study participants'
cognitive conditions (MCI vs. those with normal cognition (NC)) using facial
features. The segments and sequence information of the facial features improved
the prediction performance compared with the non-temporal features. The
detection accuracy using this combined method reached 88% whereas 84% is the
accuracy without applying the segments and sequences information of the facial
features within a video on a certain theme.

利用深度学习算法从视频中提取面部特征来检测老年人的轻度认知障碍（MCI），通过使用卷积自编码器提取空间整体面部特征和使用变换器提取时间信息的方法，该研究在 I-CONECT 行为干预研究数据中成功地检测了 MCI 病人与正常认知状况的病人，结合特定主题视频中面部特征信息的片段和序列信息可以提高预测性能，最终达到了 88% 的准确率。