Bay Labs has received US FDA 510(k) clearance for its EchoMD AutoEF software product for the fully automated clip selection and calculation of left ventricular ejection fraction. The EchoMD AutoEF algorithms are designed to eliminate the need to manually select views, choose the best clips, and manipulate them for quantification, an often time-consuming and highly variable process.
Unlike current technologies, a press release reports, EchoMD AutoEF automatically reviews all the relevant digital video clips of cardiac cycles from a patient’s echocardiography study, rates them according to image quality, and selects the best ones to calculate the ejection fraction. The press release explains that the EchoMD AutoEF software algorithm “learned” clip selection and ejection fraction calculation after being trained on a carefully curated dataset of over 4,000,000 images, representing 9,000 patients. The software should be able to be integrated into any DICOM PACS medical imaging environment and aims to provide cardiologists with results as a seamless part of routine diagnostic workflow.
Neil J Weissman (Georgetown University School of Medicine, Washington, DC, USA), comments: “Left ventricular ejection fraction has been a mainstay of echocardiography for the last 50 years. Bay Labs’ use of artificial intelligence for image selection and automated EF measurement will allow clinicians across a wide range of experience to obtain accurate evaluation of ventricular function and aid in interpretation of the echocardiograms with greater efficiency. This will ultimately result in more effective care for our patients.”
Charles Cadieu, co-founder and CEO of Bay Labs, states: “At Bay Labs, our hope is that EchoMD AutoEF will assist cardiologists in their decision making and enhance the care they provide to their patients. We look forward to continuing to develop unique deep learning technologies that enable expanded access to high-quality echocardiography image acquisition and interpretation, with the goal to improve disease management and patient outcomes through earlier detection and monitoring.”