Feasibility and ML Training Investigation of the Senseye DT for Diagnosis of Adults with PTSD

This clinical study will be conducted to inform and train the Senseye DT's machine learning algorithm, to be evaluated in a future study for effectiveness in identifying people with PTSD compared to the Clinician-Administered PTSD Scale for DSM-5, revised version (CAPS-5-R) assessment. The Senseye DT is designed to utilize video data captured from a subject's eyes while they are presented various visual stimuli to extract ocular metrics (i.e., pupil size, gaze pattern, etc.) and heart rate data and use this information to detect the presence and severity of PTSD. Due to the overlapping symptomatology between PTSD, GAD, and MDD and common elements of ANS abnormalities (e.g., hyperarousal in both PTSD and GAD), it is necessary for the Senseye DT's specificity to discern between PTSD, GAD and MDD. This study is therefore designed to fully assess the presence and severity of each of these disorders in subjects to ensure device specificity as well as to test the feasibility to diagnose and rate severity of these conditions. The study will be divided into two concurrent phases: Phase I will focus on assessing the presence of PTSD via Screening through Baseline Study Visits. The objective of this phase is to obtain an accurate baseline diagnosis and severity rating for PTSD using Standard of Care methods and to use the Senseye DT to obtain data necessary for the device to be trained to produce a PTSD diagnosis. Baseline diagnosis and severity of GAD and MDD will also be determined. Phase II will focus on assessing the severity of PTSD symptoms over time, with the objective to collect data necessary to train the Senseye DT machine learning algorithm to accurately determine a subject's PTSD severity rating correlated to the CAPS-5-R. Phase II consists of follow-up time points of 4 weeks, 8 weeks, and 12 weeks, and assesses changes in PTSD since the baseline severity determined in Phase I. Changes in GAD and MDD from baseline will also be assessed.