Abstract Traditional psychiatric assessments often depend on self-reports, which may be biased, while real-world evaluations pose safety and feasibility challenges. Specific phobias are underdiagnosed and often comorbid, with affected individuals at elevated risk for severe outcomes. This study aimed to integrate the DSM-5 diagnostic framework, Virtual Reality (VR), and Machine Learning (ML) to identify multimodal features most predictive of phobia severity. Ninety-four adults completed 100 independent trials across four VR scenarios (neutral, Cynophobia, Astraphobia, combined), during which behavioral, physiological, demographic, and self-reported measures were recorded alongside DSM-5 severity ratings. Feature selection and ML analyses revealed that Cynophobia severity was best predicted by a multimodal subset comprising age, neutral-scenario task completion time, Cynophobic virtual distance, oxygen-level variations, and DSM-5 Astraphobia severity. The next most predictive subset comprised sense of presence and DSM-5 Astraphobia severity. The Naïve Bayes classifier achieved robust performance across all features, underscoring the complementary value of multimodal inputs. These findings suggest a potential comorbidity between Cynophobia and Astraphobia and demonstrate the diagnostic relevance of integrating VR-based behavioral and physiological measures with DSM-5 criteria. The study contributes an integrated and scalable framework for enhancing the objectivity and reliability of phobia assessment and highlights future potential for VR–AI applications in clinical diagnostics. Graphical abstract
Munir et al. (Mon,) studied this question.