# Machine learning for the automated identification and tracking of rare myocardial diseases

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2020 · $687,210

## Abstract

PROJECT SUMMARY
Although cardiac amyloidosis and hypertrophic cardiomyopathy (HCM) are relatively rare causes of heart
failure (HF), they are particularly challenging to detect and treat for several shared reasons: (1) on routine
clinical imaging (i.e., echocardiography [echo]), they can be difficult to distinguish from superficially similar,
more common forms of cardiac disease that cause left ventricular (LV) hypertrophy; (2) the diagnoses are
often missed and thus patients can present late in the course of disease at a time when treatment is difficult;
(3) objective, noninvasive metrics that reliably reflect disease progression have not been identified; and (4) the
small number of known patients with these diseases can make epidemiology studies and clinical trials difficult
to organize and conduct. For both cardiac amyloidosis and HCM, echo plays a critical role in both diagnosis
and longitudinal monitoring given its ubiquitous clinical availability, safety, and low cost. More broadly, echo
dominates the current landscape of routine cardiac imaging, with tens of millions of echos performed in the
United States each year. However, the clinical challenges described above highlight several shortcomings of
echo: it is limited in its ability to (1) diagnose disease at its early stages; (2) discriminate between
morphologically similar diseases; and (3) quantify disease progression. This proposal seeks to address
deficiencies in the current echo reading workflow, which is subjective and captures only a small fraction of the
data available in each study. The overall objective of this application is to use advances in machine learning to
develop and validate fully-automated echo image analytic approaches to diagnose and track rare
cardiomyopathies, focusing on cardiac amyloidosis and HCM. Our proposal is centered on the hypothesis
that highly scalable computer vision methods can be applied to echo studies to overcome limitations
of the standard clinical echo reading workflow. Accordingly our aims are: (1) Apply an automated method
for echo quantification and disease identification to detect and differentiate cardiac diseases that cause
increased LV wall thickness; and (2) Characterize quantifiable echo measures of disease progression in
cardiac amyloidosis and HCM and associate these with clinical outcomes. Our multidisciplinary team, which is
composed of experts in cardiomyopathies, echocardiography, computer vision, and machine learning, will
analyze echos and patient data from 2 large patient registries: the Multicenter Amyloid Phenotyping Study
(MAPS) and the Sarcomeric Human Cardiomyopathy Registry (SHaRe) HCM Network, with validation using a
repository of nearly 400,000 echos. The successful completion of our aims will result in an innovative tool for
early diagnosis of myocardial diseases and tracking of disease progression. Importantly, our project will set
the stage for conducting larger epidemiology studies of rare myocardial diseases by auto...

## Key facts

- **NIH application ID:** 9739345
- **Project number:** 5R01HL140731-03
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Rahul Chandrakant Deo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $687,210
- **Award type:** 5
- **Project period:** 2018-07-05 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9739345

## Citation

> US National Institutes of Health, RePORTER application 9739345, Machine learning for the automated identification and tracking of rare myocardial diseases (5R01HL140731-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9739345. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*