Mild cognitive impairment detection from facial video interviews by applying spatial-to-temporal attention module.

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 and 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 holistic spatial facial features using a convolutional autoencoder and temporal information using transformers. We proposed the Spatial-to-Temporal Attention Module (STAM) to detect the I-CONECT study participants' cognitive conditions (MCI vs. those with normal cognition (NC)) using facial and interaction features. The interaction features of the facial features improved the prediction performance compared with applying facial features solely. The detection accuracy using this combined method reached 88%, whereas the accuracy without applying the segments and sequences information of the facial features within a video on a certain theme was 84%. Overall, the results show that spatiotemporal facial features modeled using DL algorithms have a discriminating power for the detection of MCI.

Linguistic-based Mild Cognitive Impairment detection using Informative Loss.

This paper presents a deep learning method using Natural Language Processing (NLP) techniques, to distinguish between Mild Cognitive Impairment (MCI) and Normal Cognitive (NC) conditions in older adults. We propose a framework that analyzes transcripts generated from video interviews collected within the I-CONECT study project, a randomized controlled trial aimed at improving cognitive functions through video chats. Our proposed NLP framework consists of two Transformer-based modules, namely Sentence Embedding (SE) and Sentence Cross Attention (SCA). First, the SE module captures contextual relationships between words within each sentence. Subsequently, the SCA module extracts temporal features from a sequence of sentences. This feature is then used by a Multi-Layer Perceptron (MLP) for the classification of subjects into MCI or NC. To build a robust model, we propose a novel loss function, called InfoLoss, that considers the reduction in entropy by observing each sequence of sentences to ultimately enhance the classification accuracy. The results of our comprehensive model evaluation using the I-CONECT dataset show that our framework can distinguish between MCI and NC with an average area under the curve of 84.75%.