A virtual reality-based multimodal framework for adolescent depression screening using machine learning

Background Major depressive disorder (MDD) in adolescents poses an increasing global health concern, yet current screening practices rely heavily on subjective reports. Virtual reality (VR), integrated with multimodal physiological sensing (EEG+ET+HRV), offers a promising pathway for more objective diagnostics. Methods In this case-control study, 51 adolescents diagnosed with first-episode MDD and 64 healthy controls participated in a 10-minute VR-based emotional task. Electroencephalography (EEG), eye-tracking (ET), and heart rate variability (HRV) data were collected in real-time. Key physiological differences were identified via statistical analysis, and a support vector machine (SVM) model was trained to classify MDD status based on selected features. Results Adolescents with MDD showed significantly higher EEG theta/beta ratios, reduced saccade counts, longer fixation durations, and elevated HRV LF/HF ratios (all p <.05). The theta/beta and LF/HF ratios were both significantly associated with depression severity. The SVM model achieved 81.7% classification accuracy with an AUC of 0.921. Conclusions The proposed VR-based multimodal system identified robust physiological biomarkers associated with adolescent MDD and demonstrated strong diagnostic performance. These findings support the utility of immersive, sensor-integrated platforms in early mental health screening and intervention. Future work may explore integrating the proposed multimodal system into wearable or mobile platforms for scalable, real-world mental health screening.