# An automated system to interpret echocardiography to predict adverse outcomes in patients with right ventricular dysfunction in daily hospital practice

> **NIH NIH R41** · MPROBE, INC. · 2021 · $346,545

## Abstract

Project Summary
Right ventricle (RV) dysfunction is a common and complex form of pediatric heart disease. It is
also a common contributor to morbidity and mortality for patients with congenital heart diseases
(CHD). Due to the complex geometry of the RV and its relative adaptability to changing
physiologic conditions, RV dysfunction is poorly understood and difficult to characterize
precisely and accurately, thus diagnosis is often delayed. The most common diagnosis tool is
echocardiograms. Manual review of echocardiograms is time consuming, however.
Furthermore, there might be uncovered echocardiogram patterns associated with RV
dysfunctions. In adult studies, machine learning models (MLM) have been successfully
implemented to assess RV functions by echocardiograms. We hypothesize that applying novel
MLM to pediatric echocardiograms will allow us to improve the accuracy and reliability of
assessment, as well as identify novel markers of RV dysfunction. We propose to develop an
automated tool to generate a RV health score to identify RV dysfunction and predict the
development and time of adverse outcomes including heart failure, heart and/or lung
transplantation, and death. The automated tool will constitute an early warning system module,
which will be deployed onto a big-data-based risk prediction platform developed by our small
business. The study has three specific aims. First, we will extract echocardiograms and
structured electronic medical records from the Stanford Children’s Hospital. Cohorts of children
with normal or abnormal RV will be constructed. Second, MLM will be developed and validated
to 1) predict the presence of RV dysfunction and the probability of adverse outcomes, and 2)
predict the rate of progression to adverse outcomes. A deep learning-based workflow will be
established to take input of pediatric echocardiogram and clinical data and generate predictions.
Third, we will integrate the models developed in Aim #2 into the HBI Spotlight Solutions. The
Spotlight Solutions include a healthcare surveillance platform with high-capacity data
infrastructure and risk engines to offer AI solutions to care facilities participating the Healthix,
the largest public health information exchange network in the US. This will prepare our
algorithms for further clinical validation in other cohorts.

## Key facts

- **NIH application ID:** 10326000
- **Project number:** 1R41HL160362-01
- **Recipient organization:** MPROBE, INC.
- **Principal Investigator:** JAMES W SCHILLING
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $346,545
- **Award type:** 1
- **Project period:** 2021-08-15 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10326000

## Citation

> US National Institutes of Health, RePORTER application 10326000, An automated system to interpret echocardiography to predict adverse outcomes in patients with right ventricular dysfunction in daily hospital practice (1R41HL160362-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10326000. Licensed CC0.

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