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

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R44DA058431-01
Agency Tracking Number: R44DA058431
Amount: $319,080.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NIDA
Solicitation Number: DA23-021
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-09-01
Award End Date (Contract End Date): 2024-08-31
Small Business Information
3555 S Irving
Englewood, CO 80110
United States
DUNS: 007785282
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 () -
Business Contact
Phone: (720) 203-6970
Research Institution

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 algorithmand increase sensitivities and specificities so that we have a robust archetype for OUDvs healthy patients- Collect follow-up OUD data and correlate follow-up scans with successful outcomesof rehabilitation treatment and therefore identify those addicts whose cognitive staterequires modified treatment approaches, with the aim of decreasing relapse rates andrecidivism rates.- Develop a scalable multimodal product, including EEG with ERP, for rehabilitationfacilities that is readily accessible to clinicians and create a dynamic data asset to helplongitudinally predict outcomes.

* Information listed above is at the time of submission. *

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