ArticleExpert systems with applications2024
MC-ViViT: Multi-branch Classifier-ViViT to Detect Mild Cognitive Impairment in Older Adults Using Facial Videos.
Article in Expert systems with applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Detecting cognitive impairment and psychological well-being among older adults.Machine learning. Health · 2026Article
- Facial phenotypes in Alzheimer's disease: from neurobiology to artificial intelligence.Alzheimer's research & therapy · 2026Review
- Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition.Neural computing & applications · 2026Article
- Facial Expressions as a Nexus for Health Assessment.Bioengineering (Basel, Switzerland) · 2026Review
- A comparative analysis of video vision transformers on word-level sign language datasets.PloS one · 2026Article
- Bridging Language Markers and Pathology: Correlations Between Digital Speech Measures and Surrogate CSF Biomarkers in Alzheimer's Disease.bioRxiv : the preprint server for biology · 2025Article
- AI-Based Facial Emotion Analysis for Early and Differential Diagnosis of Dementia.Bioengineering (Basel, Switzerland) · 2025Article
- Mild cognitive impairment detection from facial video interviews by applying spatial-to-temporal attention module.Expert systems with applications · 2024Article
- Bridging language markers and pathology: correlations between digital speech measures and surrogate CSF biomarkers in Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
3 authors.
Funding
Abstract
Deep machine learning models including Convolutional Neural Networks (CNN) have been successful in the detection of Mild Cognitive Impairment (MCI) using medical images, questionnaires, and videos. This paper proposes a novel Multi-branch Classifier-Video Vision Transformer (MC-ViViT) model to distinguish MCI from those with normal cognition by analyzing facial features. The data comes from the I-CONECT, a behavioral intervention trial aimed at improving cognitive function by providing frequent video chats. MC-ViViT extracts spatiotemporal features of videos in one branch and augments representations by the MC module. The I-CONECT dataset is challenging as the dataset is imbalanced containing Hard-Easy and Positive-Negative samples, which impedes the performance of MC-ViViT. We propose a loss function for Hard-Easy and Positive-Negative Samples (HP Loss) by combining Focal loss and AD-CORRE loss to address the imbalanced problem. Our experimental results on the I-CONECT dataset show the great potential of MC-ViViT in predicting MCI with a high accuracy of 90.63% accuracy on some of the interview videos.
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Registered trials
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