Validate the Temporal Stability and Responsiveness of AI-COA® for Depression and Anxiety

The SEQUOIA-2 study evaluates the effectiveness of Artificial Intelligence (AI) in measuring depression and anxiety severity in adults. Investigators from Deliberate Solutions, Inc. and Baylor College of Medicine are conducting this study to determine whether AI can provide reliable clinical assessments of mood and anxiety disorders. The study compares the tool's performance against standard clinician ratings over a 12-week period to determine its consistency and sensitivity to symptom changes. In clinical trials for new depression and anxiety treatments, human clinicians typically conduct interviews to evaluate participants' symptoms. These assessments are critical but may vary based on the clinician's experience or interview style, potentially affecting the reliability of research findings. To address this challenge, the study team developed an AI-based Clinical Outcome Assessment tool, called AI-COA®, which analyzes video interviews to measure symptoms of depression and anxiety consistently and objectively. AI-COA® has been accepted by the U.S. Food and Drug Administration (FDA) into the Innovative Science and Technology Approaches for New Drugs (ISTAND) pilot program. The primary objectives of the SEQUOIA-2 study are to confirm test-retest reliability (whether the tool produces consistent scores when symptoms are stable) and sensitivity to change (whether the tool accurately detects changes in symptom severity over time compared to standard clinical scales). Participants will undergo a series of remote, video-recorded clinical interviews conducted by trained clinicians. The study uses standard clinical outcome assessments as reference standards. The recorded data will be processed by the tool to assess its agreement with human raters. Participants will also complete questionnaires about their symptoms and perform brief tasks. The development and validation of reliable, AI-driven assessment tools through this study aim to enhance the accuracy of mental health evaluations, potentially improving the testing and approval processes for new treatments targeting depression and anxiety.