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

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41HL160362-01
Agency Tracking Number: R41HL160362
Amount: $346,545.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: NHLBI
Solicitation Number: PA20-265
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-08-15
Award End Date (Contract End Date): 2022-07-31
Small Business Information
Mountain View, CA 94043-1618
United States
DUNS: 080423971
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 (415) 350-3140
Business Contact
Phone: (650) 512-0108
Research Institution
STANFORD, CA 94305-2004
United States

 Nonprofit College or University

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.Project narrative
The machine learning models developed in this study will be applicable to any infant, child or
adolescent undergoing an echocardiogram, particularly those requiring serial exams to assess
changes from disease progression or treatment effect. It can be adopted by pediatric clinicians
to assess cardiac function with a high degree of accuracy and confidence, even for those who
have little training in echocardiography, thus improving access to healthcare and potentially
reducing costs.

* Information listed above is at the time of submission. *

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