Ignite Creation Date:
2024-05-06 @ 7:24 PM
Last Modification Date:
2024-10-26 @ 3:06 PM
Study NCT ID:
NCT06002048
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2023-08-21
First Post:
2023-07-24
Brief Title:
AI Ready and Equitable Atlas for Diabetes Insights
Sponsor:
University of Washington
Organization:
University of Washington
Study Overview
Official Title:
AI Ready and Equitable Atlas for Diabetes Insights
Status:
ENROLLING_BY_INVITATION
Status Verified Date:
2023-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
AI-READI
Brief Summary:
The study will collect a cross-sectional dataset of 4000 people across the US from diverse racialethnic groups who are either 1 healthy or 2 belong in one of the three stages of diabetes severity pre-diabetesdiet controlled oral medication andor non-insulin-injectable medication controlled or insulin dependent forming a total of four groups of patients Clinical data social determinants of health surveys continuous glucose monitoring data biomarkers genetic data retinal imaging cognitive testing etc will be collected The purpose of this project is data generation to allow future creation of artificial intelligencemachine learning AIML algorithms aimed at defining disease trajectories and underlying genetic links in different racialethnic cohorts A smaller subgroup of participants will be invited to come for a follow-up visit in year 4 of the project longitudinal arm of the study Data will be placed in an open-source repository and samples will be sent to the study sample repository and used for future research
Detailed Description:
The Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights AI-READI project seeks to create a flagship ethically-sourced dataset to enable future generations of artificial intelligencemachine learning AIML research to provide critical insights into type 2 diabetes mellitus T2DM including salutogenic pathways to return to health The ability to understand and affect the course of complex multi-organ diseases such as T2DM has been limited by a lack of well-designed high quality large and inclusive multimodal datasets The AI-READI team of investigators will aim to collect a cross-sectional dataset of 4000 people and longitudinal data from 10 of the study cohort across the US The study cohort will be balanced for self-reported raceethnicity gender and diabetes disease stage Data collection will be specifically designed to permit downstream pseudo-time manifold analysis an approach used to predict disease trajectories by collecting and learning from complex multimodal data from participants with differing disease severity normal to insulin-dependent T2DM The long-term objective for this project is to develop a foundational dataset in T2DM agnostic to existing classification criteria or biases which can be used to reconstruct a temporal atlas of T2DM development and reversal towards health ie salutogenesis Six cross-disciplinary project modules involving teams located across eight institutions will work together to develop this flagship dataset Data will be optimized for downstream AIML research and made publicly available This project will also create a roadmap for ethical and equitable research that focuses on the diversity of the research participants and the workforce involved at all stages of the research process study design and data collection curation analysis and sharing and collaboration
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| 3OT2OD032644-01S1 | NIH | None | httpsreporternihgovquickSearch3OT2OD032644-01S1 |