Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI)

Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI)

Introduction Artificial Intelligence Ready and Equitable for Diabetes Insights (AI-READI) is a data collection project on type 2 diabetes mellitus (T2DM) to facilitate the widespread use of artificial intelligence and machine learning (AI/ML) approaches to study salutogenesis (transitioning from T2D...

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Main Authors: Gerald McGwin, Linda M Zangwill, Nicholas Evans, Shannon McWeeney, Cecilia S Lee, Bhavesh Patel, Jeffrey C Edberg, Cynthia Owsley, Aaron Lee, Cecilia Lee, Sally L Baxter, Michael Snyder, Samantha Hurst, Nicole Ehrhardt, Christopher Chute, Dawn S Matthies, Julia P Owen, Amir Bahmani, Sally Baxter, Edward Boyko, Aaron Cohen, Jorge Contreras, Garrison Cottrell, Virginia de Sa, Jeffrey Edberg, Irl Hirsch, Michelle Hribar, T.Y. Alvin Liu, Bonnie Maldenado, Sara Singer, Bradley Voytek, Joseph Yracheta, Linda Zangwill
Format: Article
Language:English
Published: BMJ Publishing Group 2025-02-01
Series:BMJ Open
Online Access:https://bmjopen.bmj.com/content/15/2/e097449.full
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Summary:Introduction Artificial Intelligence Ready and Equitable for Diabetes Insights (AI-READI) is a data collection project on type 2 diabetes mellitus (T2DM) to facilitate the widespread use of artificial intelligence and machine learning (AI/ML) approaches to study salutogenesis (transitioning from T2DM to health resilience). The fundamental rationale for promoting health resilience in T2DM stems from its high prevalence of 10.5% of the world’s adult population and its contribution to many adverse health events.Methods AI-READI is a cross-sectional study whose target enrollment is 4000 people aged 40 and older, triple-balanced by self-reported race/ethnicity (Asian, black, Hispanic, white), T2DM (no diabetes, pre-diabetes and lifestyle-controlled diabetes, diabetes treated with oral medications or non-insulin injections and insulin-controlled diabetes) and biological sex (male, female) (Clinicaltrials.org approval number STUDY00016228). Data are collected in a multivariable protocol containing over 10 domains, including vitals, retinal imaging, electrocardiogram, cognitive function, continuous glucose monitoring, physical activity, home air quality, blood and urine collection for laboratory testing and psychosocial variables including social determinants of health. There are three study sites: Birmingham, Alabama; San Diego, California; and Seattle, Washington.Ethics and dissemination AI-READI aims to establish standards, best practices and guidelines for collection, preparation and sharing of the data for the purposes of AI/ML, including guidance from bioethicists. Following Findable, Accessible, Interoperable, Reusable principles, AI-READI can be viewed as a model for future efforts to develop other medical/health data sets targeted for AI/ML. AI-READI opens the door for novel insights in understanding T2DM salutogenesis. The AI-READI Consortium are disseminating the principles and processes of designing and implementing the AI-READI data set through publications. Those who download and use AI-READI data are encouraged to publish their results in the scientific literature.
ISSN:2044-6055

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