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Clinical Research
The Lancet Digital Health: Heart failure screening during ECG-enabled stethoscope examination
Read an article published by Lancet Digital Health about heart failure screening with an ECG-enabled stethoscope.
Published:
Articles
The Lancet Digital Health: Heart failure screening during ECG-enabled stethoscope examination
Read an article published by Lancet Digital Health about heart failure screening with an ECG-enabled stethoscope.
Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform
Read about the assessment of the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform.
The Power of Mobile Health: The Girl With the Gadgets in Uganda
Point of care ultrasonography and Eko's technology benefit remote clinical diagnosis and management in underserved areas around the world.
Review of Telehealth Solutions for Outpatient Heart Failure Care in a Veterans Health Affairs Hospital in the COVID-19 Era
Read about The Veterans Health Administration (VA) early adoption of telehealth for outpatient heart failure care.
IEEE (2018) - Deep Unsupervised Representation Learning for Abnormal Heart Sound Classification
This study presents a comparison of conventional and state-of-the-art deep learning based computer algorithm paradigms for the audio classification task of normal, mild abnormalities, and moderate/severe abnormalities as present in phonocardiogram recordings.
Circulation (2018) - Artificial Intelligence Detects Pediatric Heart Murmurs with Cardiologist-Level Accuracy
Deep learning has the potential to similarly transform cardiac murmur recognition through its integration with digital stethoscope technologies.
Pediatric Cardiology (2019) - Real-World Evaluation of the Eko Electronic Teleauscultation System
Their study demonstrates that Eko recordings had a high percent of agreement with in-person auscultation findings and echocardiogram findings, with moderate inter-rater reliability. It was useful in identifying patients with pathologic murmurs who would benefit from further assessment. It was able to discern major types of pathological murmurs.
Journal of Cardiac Failure (2018) - Use of Ventricular Assist Device Acoustical Signatures to Detect Device Thrombosis
Applying machine learning algorithms on pump acoustical signals provides substantial information regarding thrombosis states, and potentially allows for sensitive detection of PT assisting hemolysis biomarkers and pump powers.
AHA (2019) - Prospective Analysis of Utility of Signals from an ECG-enabled Stethoscope to Automatically Detect a Low Ejection Fraction Using Neural Network Techniques Trained from the Standard 12-lead ECG
ECG-enabled stethoscopes (ECG-steth) can acquire single lead ECGs during cardiac auscultation, and may facilitate real-time screening for pathologies not routinely identified during physical examination (eg, arrhythmias).
Nature Medicine (2019) - Screening for Cardiac Contractile Dysfunction Using an Artificial Intelligence–Enabled Electrocardiogram
In this work, researchers showed that application of artificial intelligence (AI) to the electrocardiogram (ECG) could identify ALVD. Using paired 12-lead ECG and echocardiogram data, from 44,959 patients at the Mayo Clinic, they trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone.
American Journal of Cardiology (2019) - The Digital Stethoscope – Two Senses Are Better Than One
The sound quality of the digital stethoscope is just as good, or superior to an analog stethoscope.
Handheld Wireless Digital Phonocardiography for Machine Learning-Based Detection of Aortic Stenosis
Learn How the Core Can More Accurately Detect Aortic Stenosis.
Refining Auscultation of Left Ventricular Assist Devices: Insights From Phonocardiography
Read about Auscultation of Left Ventricular Assist Devices.
Handheld Wireless Digital Phonocardiography for Machine Learning-Based Detection of Mitral Regurgitation
Read the abstract or research presented and published at the American Heart Association Scientific Sessions.
Detect Low Ejection Fraction with ECG Stethoscope
Prospective analysis of utility of signals from an Ecg-enabled stethoscope to automatically detect a Low Ejection Fraction using neural network techniques.
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