News | Cardiovascular Ultrasound | June 27, 2018

EchoMD AutoEF Software Improves Variability in Ejection Fraction Estimation

Study results show artificial intelligence-based software has less variability in evaluating left ventricular EF than the reported average variability of cardiologists

EchoMD AutoEF Software Improves Variability in Ejection Fraction Estimation

June 27, 2018 – A recent study conducted with the Minneapolis Heart Institute found that Bay Labs’ EchoMD AutoEF deep learning software has less variability in evaluating left ventricular ejection fraction (EF) than the average variability of cardiologists reported in literature. Results of the study were presented at the 2018 American Society of Echocardiography (ASE) Annual Scientific Sessions, June 22-26 in Nashville.

Literature shows that the average variability of cardiologist readers using the Simpson’s biplane method in estimating EF is 9.2 percent. The observed variability of EchoMD AutoEF was superior at 8.29 percent (p = 0.002). The study also demonstrated that EchoMD AutoEF is an accurate and fully automated method of calculating EF from complete echocardiographic patient studies without user intervention. In addition to normal patients, it performed well on obese patients, and on patients with a range of normal and abnormal EF.

“Historically there have been challenges with variability and reproducibility in reporting of the ejection fraction, especially when the EF is not normal; our study showed that the EchoMD AutoEF algorithms can aid interpretation enormously and have less variability than cardiologists reported in literature,” said Richard Bae, M.D., FACC, director of the Echocardiography Laboratory at the Minneapolis Heart Institute and co-author of the study. “By supporting fast, efficient and accurate AI [artificial intelligence]-assisted echocardiogram analysis, the algorithms can allow physicians to focus on putting results into context for the patient — guiding prognosis and course of management.”

The study included 405 echocardiographic patient studies from Minneapolis Heart Institute representing a wide range of body mass index, EF values and of ultrasound systems. For each patient study, the Bay Labs’ software automatically selected optimal apical four-chamber and apical two-chamber digital video clips and used them to perform an EF calculation. These calculations were compared to the standard Simpson’s biplane method.

For more information: www.baylabs.io

Related Content

Screening Mammography Could Benefit Men at High Risk of Breast Cancer
News | Mammography | September 18, 2019
Selective mammography screening can provide potentially lifesaving early detection of breast cancer in men who are at...
Radiation After Immunotherapy Improves Progression-free Survival for Some Metastatic Lung Cancer Patients
News | Lung Cancer | September 18, 2019
Adding precisely aimed, escalated doses of radiation after patients no longer respond to immunotherapy reinvigorates...
Noninvasive Radioablation Offers Long-term Benefits to High-risk Heart Arrhythmia Patients
News | Radiation Therapy | September 17, 2019
September 17, 2019 — Treating high-risk heart patients with a single, high dose of...
Varian Unveils Ethos Solution for Adaptive Radiation Therapy
News | Image Guided Radiation Therapy (IGRT) | September 16, 2019
At the 2019 American Society for Radiation Oncology (ASTRO) annual meeting, being held Sept. 15-18 in Chicago, Varian...
Long-term Hormone Therapy Increases Mortality Risk for Low-PSA Men After Prostate Surgery
News | Prostate Cancer | September 16, 2019
Secondary analysis of a recent clinical trial that changed the standard of care for men with recurring prostate cancer...
FDA Clears GE Healthcare's Critical Care Suite Chest X-ray AI
Technology | X-Ray | September 12, 2019
GE Healthcare announced the U.S. Food and Drug Administration’s (FDA) 510(k) clearance of Critical Care Suite, a...
iCAD's ProFound AI Wins Best New Radiology Solution in 2019 MedTech Breakthrough Awards
News | Computer-Aided Detection Software | September 09, 2019
iCAD Inc. announced MedTech Breakthrough, an independent organization that recognizes the top companies and solutions...
Imaging Biometrics and Medical College of Wisconsin Awarded NIH Grant
News | Neuro Imaging | September 09, 2019
Imaging Biometrics LLC (IB), in collaboration with the Medical College of Wisconsin (MCW), has received a $2.75 million...
ASNC Announces Multisocietal Cardiac Amyloidosis Imaging Consensus
News | Cardiac Imaging | September 09, 2019
September 9, 2019 — The American Society of Nuclear Cardiology (ASNC) published a new expert consensus document along
A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images

A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images. The algorithm, described at the SBI/ACR Breast Imaging Symposium, used deep learning, a form of machine learning, which is a type of artificial intelligence. Image courtesy of Sarah Eskreis-Winkler, M.D.

Feature | Society of Breast Imaging (SBI) | September 06, 2019 | By Greg Freiherr
The use of smart algorithms has the potential to make healthcare more efficient.