aceVirtual reality (bio-)markers differentiate ADHD from depression and controls
The heterogeneity of attention-deficit/hyperactivity disorder (ADHD) and its symptom overlap with other mental disorders limit the clinical utility of isolated diagnostic markers. Virtual reality (VR)-based paradigms offer potential to aid individual diagnostic decisions. This study investigated the diagnostic value of classification models based on multimodal data derived from a virtual email sorting task (VEST). Data were pooled from two studies and used to distinguish unmedicated participants with ADHD (n = 4…
Support vector machine models trained on VR task data distinguished ADHD from controls (0.85 balanced accuracy) and from major depression (0.75), suggesting a route to more precise diagnosis.
A model using only objective VR measures did not separate medicated from unmedicated ADHD participants significantly above chance (0.64 balanced accuracy), limiting its ability to capture medication effects.
The medicated versus unmedicated ADHD model did not perform significantly above chance, and the group sizes are small, so the findings are preliminary.
Evidence
- Peer-reviewedEuropean Archives of Psychiatry and Clinical Neuroscience2026-10-14
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Truvace Impact Record TRV-2026-1999, v1: “Virtual reality (bio-)markers differentiate ADHD from depression and controls.” Truvace, 2026-10-20. /record/TRV-2026-1999 (accessed at citation time). sha256 11258079127ecd35…
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