# Machine learning driven transthoracic echocardiographic analysis and screening for cardiac amyloidosis

> **NIH NIH R43** · VIGILANT MEDICAL, INC. · 2020 · $249,373

## Abstract

Machine learning driven transthoracic echocardiographic
analysis and screening for cardiac amyloidosis
Cardiac amyloidosis (CA) is a serious but increasingly treatable cause of heart failure. Autopsy studies have
estimated the prevalence of CA at approximately 25% of all octogenarians, and 15 to 20% of patients with aortic
stenosis. Despite the increasing prevalence of CA within the general population and specific subpopulations, its
diagnosis as a cause of heart failure is hampered by under recognition and subsequent underdiagnosis in clinical
practice. Data suggest that the average time from onset of symptoms to diagnosis is 2 years and that patients
report seeing an average of 5 physicians prior to establishing a definitive diagnosis.
Transthoracic echocardiography (TTE) testing is the most common initial evaluation because of its wide
availability. A recent utilization review in the Medicare population indicated over 7 Million echocardiographic tests
are performed each year accounting for $1.2 Billion in healthcare costs. TTEs provide comprehensive
information about cardiac structure and function, yet complexity of interpretation has limited its screening
performance in patients with CA, and diagnosis can be challenging.
Thus, our group seeks to offer a computer vision and machine learning based TTE analysis and screening
solution for CA. We are uniquely positioned for accelerated development with a cohort of 359 patients with
confirmed CA and 4,862 controls. In Phase I, we will build a deep learning neural network-based image
processing pipeline. It maps the TTE sequence into a 2-dimensional space that allows for the identification of
the 4-chamber peak diastolic and peak systolic images within the cardiac heartbeat cycle. This will enable our
screening model to recognize regional myocardial wall motion changes and hypertrophic patterns that
characterize amyloidosis in comparison to controls with normal cardiac function. The operational point defining
the performance characteristics of our screening-oriented model (including sensitivity, specificity, and negative
predictive value) will be optimized using an average weighted accuracy (AWA) approach which accounts for CA
disease prevalence along with a desired false positive and false negative tradeoff. If we are successful, we
envision a Phase II proposal to build and deploy an automated TTE analysis tool, and to evaluate it in a multi-
center clinical study. This sets the stage for our long-term goal to implement a computer assisted TTE screening
solution to improve identification and by extension care of patients with cardiac amyloidosis.

## Key facts

- **NIH application ID:** 10081836
- **Project number:** 1R43HL154896-01
- **Recipient organization:** VIGILANT MEDICAL, INC.
- **Principal Investigator:** Ricardo Henao Giraldo
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $249,373
- **Award type:** 1
- **Project period:** 2020-08-07 → 2022-08-04

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10081836

## Citation

> US National Institutes of Health, RePORTER application 10081836, Machine learning driven transthoracic echocardiographic analysis and screening for cardiac amyloidosis (1R43HL154896-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10081836. Licensed CC0.

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