# Deep Learning for Automated Aortic Stenosis and Valvular Heart Disease Detection Using a Digital Stethoscope

> **NIH NIH R44** · EKO DEVICES, INC. · 2020 · $938,347

## Abstract

Abstract
This SBIR Phase II project will develop a deep learning-based clinical decision support algorithm for detecting
and diagnosing valvular heart disease based on heart sounds recorded using the Eko Core and Eko Duo Digital
Stethoscopes. This screening tool will help to decrease the number of patients with valvular heart disease that
remain undertreated simply because their condition is not diagnosed. Auscultation is commonly the method by
which valvular heart disease is first detected, but cases often fail to be referred to echocardiography for diagnosis
because clinicians fail to detect heart murmurs, particularly in noisy or rushed environments. To address this
challenge, Eko had developed the Core, a digital stethoscope attachment that can be added in-line to a clinician’s
existing stethoscope that amplifies heart sounds and Duo, a digital stethoscope in a handheld form factor with
built-in single lead electrocardiogram. Both devices are designed to stream digitized phonocardiograms to a
smartphone, tablet or personal computer. There, the signal can be analyzed with the decision support algorithm
we will develop as part of this project. The specific aims of this study are: (1) to collect a database with condition-
specific recording labels to enable deep learning for heart sounds though clinical data collection at six clinical
sites, and (2) to develop and evaluate a collection of deep convolutional neural network-based algorithms
trained on the database. These algorithms will (2a) distinguish between systolic, diastolic and continuous
murmurs, (2b) classify aortic stenosis (AS), mitral regurgitation (MR), tricuspid regurgitation (TR), and innocent
murmurs (2c) assess the severity of AS, MR and TR. By integrating these deep learning algorithms into Eko's
mobile and cloud software platform, currently used by clinicians at over 1000 institutions worldwide, we
anticipate this algorithm will enable more accurate screening for valvular heart disease in adult patients, leading
to earlier diagnosis and better patient outcomes.

## Key facts

- **NIH application ID:** 10079904
- **Project number:** 2R44HL144297-02
- **Recipient organization:** EKO DEVICES, INC.
- **Principal Investigator:** John Maidens
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $938,347
- **Award type:** 2
- **Project period:** 2018-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10079904

## Citation

> US National Institutes of Health, RePORTER application 10079904, Deep Learning for Automated Aortic Stenosis and Valvular Heart Disease Detection Using a Digital Stethoscope (2R44HL144297-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10079904. Licensed CC0.

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