Multimodal Digital Biomarkers for Evidence-Based Mental Health Decision Support and Monitoring
AIREST (Artificial-Intelligence-based Rapid Emotional Screening Test) is an AI-driven Software as a Medical Device (SaMD) in development that uses modern AI and machine-learning methods to analyse multimodal behavioural biomarkers from short voice-and-video interactions. It combines acoustic and linguistic features of speech, vocal prosody, facial-expression dynamics and simple attention-related measures into objective, quantitative indicators of mental health.The system is intended for early detection, standardised screening and diagnostic support for mental health conditions, including depressive episodes and recurrent depressive disorder, generalised anxiety disorder, mixed anxiety and depressive disorder and post-traumatic stress disorder (PTSD). By processing a few minutes of guided interaction, AIREST generates risk scores and trend indicators that can be integrated into existing clinical workflows and electronic health records.
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Imported from OpenAIRE Ukraine | Grant 101312962.
Open sourceOverview
Grant agreement 101312962 | AIREST
Open sourceAIREST (Artificial-Intelligence-based Rapid Emotional Screening Test) is an AI-driven Software as a Medical Device (SaMD) in development that uses modern AI and machine-learning methods to analyse multimodal behavioural biomarkers from short voice-and-video interactions. It combines acoustic and linguistic features of speech, vocal prosody, facial-expression dynamics and simple attention-related measures into objective, quantitative indicators of mental health.The system is intended for early detection, standardised screening and diagnostic support for mental health conditions, including depressive episodes and recurrent depressive disorder, generalised anxiety disorder, mixed anxiety and depressive disorder and post-traumatic stress disorder (PTSD). By processing a few minutes of guided interaction, AIREST generates risk scores and trend indicators that can be integrated into existing clinical workflows and electronic health records.