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Related Experiment Video

Updated: Jun 14, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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MC-ViViT: Multi-branch Classifier-ViViT to Detect Mild Cognitive Impairment in Older Adults Using Facial Videos.

Jian Sun1, Hiroko H Dodge2, Mohammad H Mahoor3

  • 1Department Of Computer Science, University of Denver, 2155 E Wesley Ave, Denver, Colorado, 80210, United States of America.

Expert Systems with Applications
|September 6, 2024
PubMed
Summary

This study introduces a new AI model, MC-ViViT, to detect Mild Cognitive Impairment (MCI) using facial features from video chats. The model achieved 90.63% accuracy, showing promise for early MCI detection.

Keywords:
Deep LearningFacial Expression FeaturesInter- and Intra-class imbalanceMild Cognitive ImpairmentMulti-branch ClassifierTransformerViViT

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Area of Science:

  • Artificial Intelligence
  • Neuroscience
  • Medical Imaging

Background:

  • Deep learning, including CNNs, shows promise in detecting Mild Cognitive Impairment (MCI) using various data types.
  • Facial feature analysis from video data presents a novel avenue for MCI detection.

Purpose of the Study:

  • To propose and evaluate a novel Multi-branch Classifier-Video Vision Transformer (MC-ViViT) model for distinguishing MCI from normal cognition.
  • To address the challenges posed by imbalanced datasets in MCI detection.

Main Methods:

  • Development of the MC-ViViT model to extract spatiotemporal facial features from video data.
  • Augmentation of feature representations using a Multi-branch Classifier (MC) module.
  • Introduction of a novel loss function (HP Loss) combining Focal loss and AD-CORRE loss to handle imbalanced data.

Main Results:

  • The MC-ViViT model demonstrated high potential in predicting MCI.
  • Achieved an accuracy of 90.63% in predicting MCI using interview videos from the I-CONECT dataset.
  • The proposed HP Loss effectively addressed the dataset imbalance issues.

Conclusions:

  • The MC-ViViT model shows significant promise as an AI-driven tool for MCI detection.
  • Facial feature analysis from video interactions is a viable method for cognitive assessment.
  • The developed HP Loss function is effective in improving model performance on imbalanced datasets for MCI prediction.