Read about The Veterans Health Administration (VA) early adoption of telehealth for outpatient heart failure care.

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.

Deep learning has the potential to similarly transform cardiac murmur recognition through its integration with digital stethoscope technologies.

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.

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.

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).

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.

The sound quality of the digital stethoscope is just as good, or superior to an analog stethoscope.

Learn How the Core Can More Accurately Detect Aortic Stenosis.

Read about Auscultation of Left Ventricular Assist Devices.

Read the abstract or research presented and published at the American Heart Association Scientific Sessions.

Prospective analysis of utility of signals from an Ecg-enabled stethoscope to automatically detect a Low Ejection Fraction using neural network techniques.