Dementia represents a significant global health challenge, with early screening during the preclinical stage being crucial for effective management. Traditional diagnostic biomarkers for Alzheimer’s Disease, the most common form of dementia, are limited by cost and invasiveness. Mild cognitive impairment (MCI), a precursor to dementia, is currently identified through neuropsychological tests like the Montreal Cognitive Assessment (MoCA), which are not suitable for large-scale screening. Eye-tracking technology, capturing and quantifying eye movements related to cognitive behavior, has emerged as a promising tool for cognitive assessment. Subtle changes in eye movements could serve as early indicators of MCI. However, the interpretation of eye-tracking data is challenging. This study introduced a dementia screening tool, VR Eye-tracking Cognitive Assessment (VECA), using eye-tracking technology, machine learning, and virtual reality (VR) to offer a non-invasive, efficient alternative capable of large-scale deployment. VECA was conducted with 201 participants from Shenzhen Baoan Chronic Hospital, utilizing eye-tracking data captured via VR headsets to predict MoCA scores and classify cognitive impairment across different educational backgrounds. The support vector regression model employed demonstrated a high correlation (0.9) with MoCA scores, significantly outperforming baseline models. Furthermore, it established optimal cut-off scores for identifying cognitive impairment with notable sensitivity (88.5%) and specificity (83%). This study underscores VECA’s potential as a portable, efficient tool for early dementia screening, highlighting the benefits of integrating eye-tracking technology, machine learning, and VR in cognitive health assessments.

Background: Dementia is a rapid growing global health challenge, and early screening in the preclinical stage is necessary. Mild cognitive impairment (MCI) is considered a transitional stage preceding dementia, and current diagnostic markers for AD are limited by cost and invasiveness. Neuropsychological tests (such as MMSE, MoCA) are valid but neither simple nor efficient enough to serve as large-scale dementia screening tools. Eye-tracking data can be encoded as cognitive activity and states, which provides quantitative and multi-dimensional attributes of cognitive function. Its application to cognitive assessment has shown promise in identifying patients with MCI or dementia. Immersive environments of virtual reality technology guarantee the integrity of eye-tracking data and the portability of VR headset enables efficient large-scale early screening of cognitive impairment in communities. Objective: Develop a 5-minute dementia screening tool — VR Eye Movement Cognitive Assessment to help physicians detect cognitive impairment as an alternative approach of traditional paper-based instruments. Methods: 201 subjects from Shenzhen Baoan Chronic Hospital were administered MoCA and VR eye movement cognitive assessments. Raw gaze data was captured by 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. 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 cognitively impaired subjects — with overall sensitivity of 88.5% and specificity of 83%. Conclusion: VR eye movement cognitive assessment is a portable, efficient, and quantitative dementia screening tool, which can be used for early screening of mild cognitive impairment and dementia.

Virtual reality (VR) technology is increasingly important in various fields, such as medicine, engineering, and science, and can be used for training and education, clinical assessment, and re- habilitation. VR is widely used in rehabilitation to evaluate and intervene in various diseases, with the potential to improve the balance and gait of patients with neurological disorders and provide additional benefits when combined with traditional rehabilitation methods. However, current VR training systems lack methods and technologies for functionally quantifying training performance. This study implemented VR head-mounted display technology and motion sensing recognition technology to achieve positioning and free movement in a virtual reality space, using motion inter- action functions in different movement modalities to achieve trunk control and balance training. Functional quantification evaluation of trunk control and balance was achieved through algorithms that include movement trajectory, trunk activity, and three-dimensional interval motion perfor- mance. The rehabilitation process of the trainees was digitally captured and updated in real-time to form a functional report, allowing medical staff and patients to understand the rehabilitation progress in real-time and promote rehabilitation effectiveness.

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.