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Bridge2AI: Salutogenesis Data Generation

Awardee
Organization

Project

Project
Number
1OT2OD032644-

01

Contact
PI/Project
Leader

LEE, AARON
Other PIs

 Description

Abstract Text

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 thePrivacy  -  Terms

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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.

Public Health Relevance Statement

Public Health Relevance Statement Recent advances in
artificial intelligence (AI) research are poised to provide
breakthrough discoveries, but have been limited by the
lack of large, well-characterized comprehensive datasets
that capture molecular, physiological, pathological, and
clinical at various stages of illness. To address these
challenges, the AI-READI team of investigators will

generate an ethically-sourced and unique dataset with
many types of data collected from patients with different
severities of type 2 diabetes mellitus (T2DM), which will
enable key discoveries about the trajectory of this disease

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and how improvements to health (i.e., salutogenesis) can

be promoted over time. The project will train future

scientists in AI-based research and establish best

practices for the generation of future datasets that are

ethically sourced and accessible for responsible and

scientifically valid use by the greater research community.

NIH Spending Category

American Indian or Alaska Native

Bioengineering

Clinical Research

Data Science

Diabetes

Health Disparities

Machine Learning and Artificial Intelligence

Minority Health

Networking and Information Technology R&D
(NITRD)

Prevention

Project Terms

Address

Affect

Artificial Intelligence

Asian

Atlases

Awareness

Behavioral

Black race

Bridge to Artificial Intelligence

Classification

Clinical

Cohort Studies

Collaborations

Communities

Complex

Consultations

Data

Data Collection

Data Set

Development

Diabetes Mellitus

Disease

Ethics

FAIR principles

Foundations
Read More

Funding

Future

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 Details

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Contact PI/
Project
Leader

Name
LEE, AARON


Title
ASSISTANT
PROFESSOR

Contact

View
Email

Program
Official

Name
KUXHAUS,
LAUREL
CATHERINE

Contact

View
Email

Other PIs









ISHIKAWA,

Name
BAXTER,
SALLY LIU 
CHUTE,
CHRISTOPHER
G 
COLLINS,
MEGAN E 

FERRYMAN,
KADIJA 
HRIBAR,
MICHELLE 
HURST,
SAMANTHA

HIROSHI 

LIU, ALVIN Y
LEE,

CECILIA
SUNGMIN 
MCGWIN,
GERALD 
MCWEENEY,
SHANNON K.
NEBEKER,

CAMILLE 

OWSLEY,
CYNTHIA 
PATEL,
BHAVESH 
SNYDER,
MICHAEL P.
SINGER,

SARA JEAN

YRACHETA,
JOSEPH
MANUEL 

ZANGWILL,
LINDA M 










Organization

Name
UNIVERSITY OF WASHINGTON

City
SEATTLE

Country
UNITED STATES (US)

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Department Type
OPHTHALMOLOGY

Organization Type
SCHOOLS OF MEDICINE

State Code
WA

Congressional District
07

Other Information

Opportunity Number
OTA-21-008

Study Section
Data Coordination, Mapping, and Modeling[DCMM]

Fiscal Year
2022

Award Notice Date
01-September-2022

Administering Institutes or Centers
NIH Office of the Director

Assistance Listing Number
93.310

DUNS Number
605799469

UEI
HD1WMN6945W6

Project Start Date

01-September-2022

Project End Date
31-August-2025

Budget Start Date
01-September-2022

Budget End Date

31-August-2025

No Cost Extension

N

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Project Funding Information for 2022

Total Funding
$5,026,499

Direct Costs
$4,569,437

Indirect Costs
$457,062

Year
Year

Funding IC

Funding IC

FY Total Cost by IC

2022 NIH Office of the Director

$5,026,499

NIH Categorical Spending
Click here for more information on NIH Categorical

Spending

Funding
IC

RM

RM

FY Total Cost by IC NIH Spending Category

$5,878,533

3641; 3584

$7,838,044

44; 101; 176; 4531; 224;
4372; 329; 701

 Sub Projects

No Sub Projects information available for
1OT2OD032644-01

 Publications

 Disclaimer

No Publications available for 1OT2OD032644-01

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 Patents

No Patents information available for
1OT2OD032644-01

 Outcomes

The Project Outcomes shown here are displayed verbatim as submitted
by the Principal Investigator (PI) for this award. Any opinions, findings,
and conclusions or recommendations expressed are those of the PI
and do not necessarily reflect the views of the National Institutes of
Health. NIH has not endorsed the content below.

No Outcomes available for 1OT2OD032644-01

 Clinical Studies

No Clinical Studies information available for
1OT2OD032644-01

 News and More

Related News Releases

No news release information available for
1OT2OD032644-01

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 History

No Historical information available for
1OT2OD032644-01

 Similar Projects

No Similar Projects information available for
1OT2OD032644-01

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