RO-Crate Summary
ROCrate ID
ark:59853/rocrate-b2ai-aireadi-release-3-0-0
Description
The Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights (AI-READI) project seeks to create a flagship ethically-sourced dataset to enable future generations of artificial intelligence/machine learning (AI/ML) 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 4,000 people and longitudinal data from 10% of the study cohort across the US. The study cohort will be balanced for self-reported race/ethnicity, 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 (i.e., salutogenesis). Data will be optimized for downstream AI/ML research and made publicly available
This dataset contains data from 2280 participants that was collected between July 19, 2023 and May 01, 2025. Data from multiple modalities are included. A full list is provided in the Data Standards section below. The data in this dataset contain no protected health information (PHI). Information related to the sex and race/ethnicity of the participants as well as medication used has also been removed.
The dataset contains 356,343 files and is around 3.82 TB in size.
A detailed description of the dataset is available in the AI-READI documentation for v3.0.0 of the dataset at docs.aireadi.org.
Authors
AI-READI Consortium
Publisher
AI-READi Consortium
Principal Investigator
Aaron Lee, Department of Ophthalmology, University of Washington
Confidentiality Level
HL7:2N (normal)
Keywords
diabetes mellitus, Machine Learning, Artificial Intelligence, Electrocardiography, Continuous Glucose Monitoring, Retinal Imaging, Eye Exam
Citation
https://docs.aireadi.org
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