Detection of Mild Cognitive Impairment Using Facial Features in Video
  Conversations
Muath Alsuhaibani Hiroko H. Dodge Mohammad H. Mahoor
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.