Assess the Utility of a Speech-based Machine Learning Algorithm to Predict Treatment Response to Psychiatric Interventions

This observational cohort study (n=200) will assess whether a speech-based machine learning algorithm can predict treatment response to psychiatric interventions, specifically repetitive transcranial magnetic stimulation (TMS) and Spravato (esketamine) nasal spray.

Conducted by Psyrin, this study focuses on individuals diagnosed with major depressive disorder (MDD), post-traumatic stress disorder (PTSD), bipolar disorder (BD), generalised anxiety disorder (GAD), or obsessive-compulsive disorder (OCD). Participants, aged 18-68, will record 12-minute speech samples before treatment, daily during treatment, immediately after, and four weeks later. Researchers will analyse speech patterns and compare them with clinical outcomes, measured by symptom severity changes on the Clinical Global Impression scale. The study aims to develop a non-invasive tool to help doctors predict which treatment is more likely to be effective for individual patients.

The study will take place over 12 months at two locations in the United States (California and New York). It will also assess whether speech assessments cause any distress to participants. If successful, this research could improve personalised treatment approaches and optimise healthcare resource allocation.

Topic Obsessive-Compulsive Disorder Bipolar Disorder Depression PTSD Anxiety Neuroscience
Compound Ketamine
Country United States of America
Visit trial

Trial Details



Trial Number

Data attribution

A large set of the trials in our database are sourced from ClinicalTrials.gov (CTG). We have modified these post to display the information in a more clear format or to correct spelling mistakes. Our database in actively updated and may show a different status (e.g. completed) if we have knowledge of this update (e.g. a published paper on the study) which isn't reflected yet on CTG. If a trial is not sourced from CTG, this is indicated on this page and you can follow the link to the alternative source of information.