Bridge2AI:Salutogenesis Data Generation Project

About This Grant

Abstract Text The Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights (AI-READI) project is one of the data generation projects in the NIH Common Fund’s Bridge2AI program. The 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, and large multimodal datasets. The 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 diabetes disease stage. Data collection will be specifically designed to permit downstream pseudotime 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 diabetes, agnostic to existing classification criteria, which can be used to reconstruct a temporal atlas of T2DM development and reversal towards health (i.e., salutogenesis). Six cross-disciplinary project modules involving teams located across eight institutions will work together to develop this flagship dataset. All data will be optimized for downstream AI/ML research and made publicly available. . The AI-READI project will also engage in a tribal consultation to address barriers and facilitators of participation with the goal of collecting similar data within a Native American cohort in an ethical and respectful manner. Specific aims include 1) Collect and share the dataset for AI/ML research according to the Findable, Accessible, Interoperable, Reusable (FAIR) data principles, 2) Create a model for developing large scalable datasets, and 3) Increase access to and quality of AI/ML research by recruiting and training personnel.