Personalized Virtual Reality Exposure for MRI Preparation Using Adaptive Anxiety Prediction Models
DOI:
https://doi.org/10.66238/jaeee24Keywords:
Personalized virtual reality, MRI anxiety, adaptive exposure, physiological feedback, machine learning in healthcareAbstract
Individual variability in anxiety response necessitates personalized preparation strategies prior to MRI. This study introduces an adaptive VR system that adjusts exposure intensity based on physiological and behavioral indicators. A machine learning model trained on 124 prior VR sessions predicted anxiety levels using heart rate, gaze fixation, and interaction latency. In a prospective study of 60 adult patients, VR exposure scenarios were dynamically modified to match predicted anxiety thresholds. Personalized VR exposure reduced STAI-state scores by 36.8%, compared with 21.5% in a non-adaptive VR control group (p < 0.05). Mean heart rate decreased by 9.4 bpm during exposure. Scan completion without interruption reached 98.3%. Adaptive personalization enhances VR-based MRI preparation efficacy by tailoring exposure intensity to patient-specific responses.
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Copyright (c) 2026 Yong Loo Lin, Daniel Kai Wei Ting, Cheryl Siok Hoon Lim
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