TY - GEN
T1 - Exploring the Multimodal Integration of VR and MRI biomarkers for Enhanced Early Detection of Mild Cognitive Impairment
AU - Park, Bogyeom
AU - Kim, Yuwon
AU - Park, Jinseok
AU - Choi, Hojin
AU - Kim, Seong Eun
AU - Ryu, Hokyoung
AU - Seo, Kyoungwon
N1 - Publisher Copyright:
© 2024 Association for Computing Machinery. All rights reserved.
PY - 2024/5/11
Y1 - 2024/5/11
N2 - Early detection of mild cognitive impairment (MCI) is crucial to impede dementia progression. Virtual reality (VR) biomarkers are adept at detecting impairments in instrumental activities of daily living (IADL), whereas magnetic resonance imaging (MRI) biomarkers excel in measuring observable structural changes in the brain. However, the efficacy of integrating VR and MRI biomarkers to improve early MCI detection remains unclear. This study aims to evaluate and compare the effectiveness of VR and MRI biomarkers and investigates the potential of their combined use for more accurate early MCI detection. Through support vector machine analysis, distinct characteristics were observed. For identifying MCI, VR biomarkers demonstrated high specificity (90.0%), whereas MRI showed high sensitivity (90.9%). The combination of both biomarkers yielded superior results in accuracy (94.4%), sensitivity (100.0%), and specificity (90.9%). Drawing from these results, we suggest a sequential diagnostic approach, employing VR for initial screening and MRI for subsequent confirmation of MCI.
AB - Early detection of mild cognitive impairment (MCI) is crucial to impede dementia progression. Virtual reality (VR) biomarkers are adept at detecting impairments in instrumental activities of daily living (IADL), whereas magnetic resonance imaging (MRI) biomarkers excel in measuring observable structural changes in the brain. However, the efficacy of integrating VR and MRI biomarkers to improve early MCI detection remains unclear. This study aims to evaluate and compare the effectiveness of VR and MRI biomarkers and investigates the potential of their combined use for more accurate early MCI detection. Through support vector machine analysis, distinct characteristics were observed. For identifying MCI, VR biomarkers demonstrated high specificity (90.0%), whereas MRI showed high sensitivity (90.9%). The combination of both biomarkers yielded superior results in accuracy (94.4%), sensitivity (100.0%), and specificity (90.9%). Drawing from these results, we suggest a sequential diagnostic approach, employing VR for initial screening and MRI for subsequent confirmation of MCI.
KW - Biomarker
KW - Magnetic resonance imaging
KW - Mild cognitive impairment
KW - Virtual reality
UR - https://www.scopus.com/pages/publications/85194195293
U2 - 10.1145/3613905.3651108
DO - 10.1145/3613905.3651108
M3 - Conference contribution
AN - SCOPUS:85194195293
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2024 - Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Sytems
PB - Association for Computing Machinery
T2 - 2024 CHI Conference on Human Factors in Computing Sytems, CHI EA 2024
Y2 - 11 May 2024 through 16 May 2024
ER -