Development of an AI-empowered device that utilizes multimodal data-visualization to aid in the diagnosis, and treatment, of OUD

点击翻译按钮获取中文摘要

Abstract Assessing the effectiveness of opioid use disorder (OUD), where high relapse rates create financial and social tolls, is a pressing clinical problem in need of better measurement tools. The ability to identify cognitive changes should improve outcomes but current intake into rehabilitation programs doesn’t typically include cognitive tests. Simple, quick, cost-effective, and objective measures are needed. This is the problem this proposal seeks to address. This proposal utilizes the experience of 8 different rehab clinics which serve 150 patients weekly, and WAVi, a commercialized brain-assessment platform that combines EEG evoked responses (ERP) with 5 other tests also sensitive to addiction (heart rate variability, physical reaction times, MoCA, Trail Making, and Flanker). This user-friendly platform focuses on minimizing testing times and cost while maximizing information. For this fast-track application, we will have the following milestones: - Collect more OUD data from different clinics to refine existing clustering algorithm and increase sensitivities and specificities so that we have a robust archetype for OUD vs healthy patients - Collect follow-up OUD data and correlate follow-up scans with successful outcomes of rehabilitation treatment and therefore identify those addicts whose cognitive state requires modified treatment approaches, with the aim of decreasing relapse rates and recidivism rates. - Develop a scalable multimodal product, including EEG with ERP, for rehabilitation facilities that is readily accessible to clinicians and create a dynamic data asset to help longitudinally predict outcomes.