Clinical Research Directory

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

Sponsor: Psyrin Inc.

Summary

This is a research study looking at whether the way people speak can help predict how well they'll respond to certain mental health treatments. The Main Goal: The researchers want to see if computer analysis of a person's speech patterns can predict whether they'll respond well to two specific treatments: TMS (Transcranial Magnetic Stimulation) and Spravato (a nasal spray medication). They're focusing on people with depression, bipolar disorder, OCD, anxiety, and PTSD. How It Works: 200 people with these conditions will participate in the study.Participants will record themselves speaking for about 12 minutes, responding to six different prompts.They'll do these recordings before treatment starts, daily during treatment, right after treatment ends, and again four weeks later. Doctors will track how well people are doing using various questionnaires and rating scales The researchers will look for connections between speech patterns and treatment success. The study will last 12 months. What Makes Someone a "Treatment Success": The study considers treatment successful if a person's symptoms improve significantly (specifically, a 2-point or greater reduction on a clinical rating scale (called Clinical Global Impression) and stays improved during the follow-up period (4-weeks). Why This Matters: If successful, this research could lead to a simple, non-invasive way to help doctors predict which treatments might work best for different patients. This could help people get the most effective treatment more quickly and help healthcare providers use their resources more efficiently. Safety Consideration The researchers will also check whether doing the speech assessments causes any distress to participants, making sure the evaluation process itself is safe and comfortable.

Official title: An Observational, Prospective Study to Assess the Utility of a Speech-based Machine Learning Algorithm to Predict Treatment Response to Psychiatric Interventions

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