ORLANDO, Fla., April 8, 2019 /PRNewswire-PRWeb/ -- DiA Imaging Analysis, provider of advanced AI-based ultrasound analysis technology, today announced the launch of LVivo SAX, a cardiac analysis tool that helps clinicians quickly and accurately interpret ultrasound images and assess heart functionality among patients suspected of suffering from acute coronary syndrome (ACS).
LVivo SAX uses artificial intelligence (AI) to analyze segmental left ventricle wall motion using the parasternal short axis view, which is a common cardiac view used in point-of-care ultrasound settings as it provides views of cardiac tissue supplied by all three major coronary vessels and is relatively easy to acquire without manipulating the patient's posture. The tool is designed to provide medical clinicians who have varying levels of ultrasound analysis or cardiological experience the ability to automatically measure, track and evaluate cardio functions, and to detect abnormalities that reduce variability and increase efficiency.
"LVivo SAX is the first AI-based, automated ultrasound tool that can be used by medical clinicians, with varying levels of experience, to detect signs of coronary heart disease from the short axis view," said Hila Goldman Aslan, DiA's CEO and Co-Founder. "Millions of people visit hospital emergency rooms each year with symptoms related to ACS, or heart attack. Electrocardiograms aren't always conclusive, and abnormalities can be difficult to detect on an ultrasound image with the naked eye. With LVivo SAX, clinicians quickly receive reliable and actionable information that can be used to analyze the heart's functionality, detect abnormalities and ensure the patient receives the required treatment as soon as possible."
Emergency room and point-of-care clinicians typically order an electrocardiogram (ECG), a blood test and an ultrasound when a patient presents with ACS symptoms. The ultrasound images, which are vitally important given that ECG and blood tests are not always conclusive, are typically viewed visually. The results are often dependent on the clinician's level of training and experience. As an integrated part of an ultrasound and IT system, LVivo SAX leverages AI to bring objectivity to the process to provide an automated and objective assessment of left ventricular function and segmental function.
"LVivo SAX has the potential to change the way emergency department clinicians manage patients with suspected ACS," said Dr. Chris Moore, Associate Professor of Emergency Medicine at Yale University School of Medicine. Dr. Moore will be conducting a post-market clinical study of LVivo SAX within Yale's emergency room environment.
"Today, the accurate assessment of ultrasound images often depends on the expertise of the interpreter. By delivering reliable and reproducible information related to wall motion abnormalities, LVivo SAX helps doctors arrive at the correct diagnosis and guide the proper care of the patient. We are pleased to take part in DiA's launch of LVivo SAX and look forward to trialing the tool in our emergency department."
DiA will demonstrate LVivo SAX, as well as its extended LVivo Cardiac Toolbox, at Booth 507 at this week's AIUM 2019 conference taking place in Orlando through April 10. To schedule a demo please contact [email protected].
About DiA Imaging Analysis
DiA Imaging Analysis provides advanced AI-based ultrasound analysis technology that makes ultrasound accessible to all. DiA's automated tools deliver fast and accurate clinical indications to support the decision-making process and offer better patient care. DiA's AI-based technology uses advanced pattern recognition and machine-learning algorithms to automatically imitate the way the human eye detects image borders and identifies motion. Using DiA's tools provides automated and objective AI tools, helps reduce variability among users, and increases efficiency. It allows clinicians with various levels of experience to quickly and easily analyze ultrasound images.
For additional information, please visit http://www.dia-analysis.com.
SOURCE DiA Imaging Analysis