Mental Health, Intellectual and Neurodevelopmental Disorder Detection With Artificial Intelligence Models

Mental Health, Intellectual and Neurodevelopmental Disorder Detection With Artificial Intelligence Models: Testing Speech-Based Machine Learning Algorithms for Clinical Assessment and Risk Stratification in Mental Health Presentations

This study investigates whether AI-driven analysis of speech can accurately predict clinical diagnoses and assess risk for various mental or behavioral health conditions, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, generalized anxiety disorder, major depressive disorder, obsessive compulsive disorder (OCD), post-traumatic stress disorder (PTSD), and schizophrenia. We aim to develop tools that can support clinicians in making more accurate and efficient diagnoses.

Inclusion Criteria

1. Participants aged between 16 and 60 years.
2. Individuals currently undergoing or referred for clinical assessment of mental or behavioral health conditions (including but not limited to ADHD, ASD, BPAD, GAD, MDD, OCD, PTSD, SSD)
3. Fluent in English
4. Capable of providing informed consent, or in the case of minors, having a parent or legal guardian who can provide consent on their behalf.
5. Access to a device (smartphone, tablet, or computer) with a microphone and stable internet connectivity, necessary for completing the speech tasks.

Exclusion Criteria

1. Individuals experiencing acute mental health crises or severe symptoms that would preclude meaningful participation in the study, including acute intoxication.
2. Severe cognitive impairment or intellectual disability that would prevent understanding of the study procedures or completion of the speech tasks.
3. Lack of fluency in English.
4. Technical limitations: Inability to access a suitable device or internet connection for completing the speech tasks