# Spatiotemporal and Deep Learning Analysis of Cardiac Imaging for Predictive Risk Stratification in Duchenne Muscular Dystrophy

> **NIH NIH F30** · PURDUE UNIVERSITY · 2022 · $54,252

## Abstract

PROJECT SUMMARY/ABSTRACT
Heart disease is the leading cause of death for individuals with Duchenne muscular dystrophy (DMD). DMD is
a devastating and progressive neuromuscular disease with no known cure. This X-linked genetic disorder
affects nearly 1 in 5000 boys and manifests as debilitating muscle weakness and progressive cardiomyopathy
(CM). While CM in some individuals with DMD progresses rapidly and fatally in their teenage years, others can
live relatively symptom-free into their thirties or forties. Early identification and treatment can improve quality
and length of life, but currently, there are no standard imaging biomarkers that can detect early onset or rapidly
progressing DMD CM. Additionally, research in this area has lagged due to small population study sizes and
limited standardized imaging data. To that end, this project will utilize the largest standardized imaging data
registry of DMD CM created through the collaboration of 6 of the largest medical institutions with DMD CM
expertise. Following the objective set up by the National Heart, Lung, and Blood Institute (NHLBI) to
“develop and optimize novel diagnostic and therapeutic strategies to prevent, treat, and cure HLBS
diseases” we propose the following Aim: 1) Identify imaging biomarkers of DMD CM onset and progression
using novel image analysis. Utilizing recently developed methods of spatiotemporal analysis of 4D (3D plus
time) cardiac imaging data, we can evaluate localized kinematic parameters in the heart that may be sensitive
to subtle changes in disease physiology. With this project we also follow a second major objective set forth by
NHBLI to “Leverage emerging opportunities in data science to open new frontiers in HLBS research”
through another Aim: 2) Apply deep learning neural network to DMD registry to evaluate CM onset and
progression. Utilizing the large DMD CM imaging registry, we will apply deep learning techniques for
automated segmentation and analysis of cardiac parameters to evaluate patterns of early-onset and rapid
progression. These results will help to bridge a crucial gap in optimizing clinical treatment for a devastating
pediatric disease and pave the way for future research and innovation through the definition of robust imaging
This fellowship research and training will be carried out at Purdue
University under the direct mentorship of Craig Goergen, PhD who is a leading expert in cardiovascular
imaging and biomechanics research and at Indiana University School of Medicine with Larry Markham, MD,
Division Chief of Pediatric Cardiology and renowned physician scientist with a focus on DMD CM. Guang Lin,
PhD (Purdue University), a data science expert, will provide expertise in the deep learning algorithm
development. Han Kor, MD (Nationwide Children’s Hospital), May Ling Mah, MD (Nationwide Children’s
Hospital), and Jonathan Soslow, MD (Vanderbilt School of Medicine) are all practicing pediatric cardiologists
with expertise in DMD cardiac imaging and...

## Key facts

- **NIH application ID:** 10536912
- **Project number:** 1F30HL162452-01A1
- **Recipient organization:** PURDUE UNIVERSITY
- **Principal Investigator:** Conner Earl
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $54,252
- **Award type:** 1
- **Project period:** 2022-08-05 → 2026-08-04

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10536912

## Citation

> US National Institutes of Health, RePORTER application 10536912, Spatiotemporal and Deep Learning Analysis of Cardiac Imaging for Predictive Risk Stratification in Duchenne Muscular Dystrophy (1F30HL162452-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10536912. Licensed CC0.

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