The VR Eye-tracking Cognitive Assessment (VECA): A Portable and Efficient Dementia Screening Tool Using Eye-Tracking Technology, Machine Learning, and Virtual Reality
Authors: Yuan Qing , Xu Ying , Zhang Xu , Zhang Chi , Pan Baobao 
Published: January 2024

Abstract

Background:Dementia is a significant global health challenge, and early screening during the preclinical stage is crucial. However, current diagnostic biomarkers for Alzheimer's Disease, the most common cause of dementia, have limitations in terms of cost and invasiveness. Mild cognitive impairment (MCI) is recognized as a transitional stage preceding dementia. Whileneuropsychological tests like the Montreal Cognitive Assessment (MoCA) are effective for identifying MCI, they are not suitable for large-scale dementia screening.
Eye-tracking technology has emerged as a promising tool for cognitive assessment by capturing and quantifying eye movements related to cognitive behavior. Subtle changes in eye movements can potentially serve as biomarkers for early MCI identification. However, interpreting the vast amount of eye-tracking data poses challenges. To address this, machine learning methods in computer science can be applied to analyze eye-tracking data and identify patterns or abnormalities indicative of MCI. Machine learning models trained on large datasets can improve the accuracy and efficiency of MCI identification. Additionally, the immersive nature of virtual reality (VR) technology allows for uninterrupted eye-tracking processes, while the portability of VR head-mounted devices enables efficient and large-scale early screening for community cognitive impairment.
Objective: Develop a dementia screening tool, VR Eye-tracking Cognitive Assessment (VECA), using eye-tracking technology, machine learning, and virtual reality as an alternative to traditional neuropsychological tests. This tool aims to help physicians detect cognitive impairment, particularly in the early stages of MCI, on a larger scale.
Methods: 201 subjects from Shenzhen Baoan Chronic Hospital were administered MoCA test and VECA. Raw gaze data were captured by the eye tracker of the VR headset and filtered as eye movements which would be encoded as features. Machine learning models were established as the predictor of MoCA score and the classifier of cognitive impairment of three education-based groups within which optimal cut-off score was given. The study has been approved by Shenzhen Baoan Chronic Hospital Ethics Committee and all subjects have signed written informed consent for participation.
Results: Support vector regression was proposed as the VR-AI model and achieved high correlation of 0.9 with MoCA score, greater than baseline model of 0.58. Optimal cut-off scores (less than 6 years of education: 14/15; 6 to 9 years of education: 18/19; more than 9 years of education: 23/24) can well distinguish normal and cognitive impairment subjects -with overall sensitivity of 88.5% and specificity of 83%.
Conclusion:VECA is a portable and efficient dementia screening tool that utilizes eye-tracking technology, machine learning, and virtual reality. It offers a quantitative approach for large-scale early screening of dementia.