# Deep Learning of Pancreas MRI to Predict Progression of T1D.

> **NIH NIH R03** · UNIVERSITY OF TEXAS AT AUSTIN · 2022 · $158,500

## Abstract

Project Summary
The TrialNet Pathway to Prevention study has provided crucial early screening for relatives of individuals with
type 1 diabetes (T1D). The presence of autoantibodies conveys high risk for progression from Stage 1 T1D,
defined by the presence of multiple diabetogenic autoantibodies, to Stage 3 T1D, or symptomatic disease.
However, the time to progression can be variable. A variety of genetic and metabolic indices have attempted to
predict progression of T1D, with varying degree of success. Additional biomarkers are needed to improve
prediction of progression, and these biomarkers must be correlated with immunological markers or metrics that
assess beta cell function.
The overall goal of the proposed study is to establish an imaging biomarker to predict progression. We propose
to improve T1D prediction by 1) co-registering longitudinal MRI taken during progression of T1D to identify spatial
evolution characteristic of disease evolution, 2) harnessing deep learning techniques to identify image features
characteristic of the pancreas in T1D, and 3) integrated imaging and functional metrics to build a predictive model
of T1D progression. This work builds upon work we have performed indicating that pancreas size, shape, and
structure are altered in new onset type 1 diabetes. These imaging metrics are also altered in individuals at risk
for developing T1D.
This study will identify imaging features characteristic of the pancreas that accompany progression to T1D. The
techniques developed may prove useful for monitoring patients at risk for T1D and predicting progression to
symptomatic disease, which is associated with lower incidence of diabetic ketoacidosis at diagnosis, better
glycemic control, and corresponding improvements in long-term complications. The ability to predict progression
would further facilitate the design of new therapeutic trials which are shorter and less expensive by stratifying
patient populations and providing intermediate end points.

## Key facts

- **NIH application ID:** 10458081
- **Project number:** 5R03DK129979-02
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** JOHN MICHAEL VIROSTKO
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $158,500
- **Award type:** 5
- **Project period:** 2021-07-28 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10458081

## Citation

> US National Institutes of Health, RePORTER application 10458081, Deep Learning of Pancreas MRI to Predict Progression of T1D. (5R03DK129979-02). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10458081. Licensed CC0.

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