Analytics 4 Life to Present New Clinical Data on Novel Cardiac Imaging Technology Using Machine-Learned Algorithms at the Transcatheter Cardiovascular Therapeutics (TCT) 2017 Scientific Symposium

Initial Data from Ongoing Study Evaluates Diagnostic Performance in Obese, Elderly, and Female Patients

Dr. Thomas Stuckey to Lead Innovation Session Discussion on Non-invasive Detection of Coronary Artery Disease Using Resting Phase Signals   

RESEARCH TRIANGLE PARK, N.C. and TORONTO, Oct. 30, 2017 /PRNewswire/ -- Analytics 4 Life, a digital health company applying the power of artificial intelligence to develop solutions that improve existing care pathways, today announced it will be presenting new clinical data on the Company's ongoing Coronary Artery Disease Learning and Algorithm Development (CADLAD) study at the Transcatheter Cardiovascular Therapeutics (TCT) 2017 scientific symposium in Denver.

"We are encouraged by these initial results which suggest that our cardiac imaging technology has clinically significant utility in assessing coronary artery disease (CAD), without the need for radiation, exercise or pharmacologic stress," said Don Crawford, CEO, Analytics 4 Life. "Conventional CAD detection pathways may be less accurate in specific populations, such as obese, elderly and female patients, but these early results show a promising potential for alternative technology."

The CADLAD study is a two-stage clinical trial at 13 sites in the U.S. measuring the diagnostic performance of the company's cardiac Phase Space Tomography Analysis (cPSTA) System in assessing cardiac health related to the presence of CAD. The cPSTA System, or CorVista™, is a non-invasive, physician-directed diagnostic test that uses a hand-held device to scan intrinsic signals from the body without radiation, contrast agents, or cardiac stress. The signal data is then transmitted to a secure, cloud-based repository for analysis, and a report is generated to help physicians assess the presence of CAD.

The ongoing study is designed to test the utility of cPSTA in a large population and other important subgroups. Enrollment into the study's first stage, focused on product development, is complete with enrollment in the study's second stage finishing before the end of this year. Results will be available early 2018 and will support the company's U.S. FDA regulatory application.

Details of the two poster presentations and lecture at the TCT 2017 scientific symposium are as follows:

Title: Noninvasive Detection of Coronary Artery Disease Using Resting Phase Signals and Advanced Machine Learning
Presenter: Dr. Thomas Stuckey, Clinical Professor of Medicine, UNC School of Medicine, Cone Health Heart and Vascular Center Greensboro, NC
Session Title, Date and Time: Interventional Innovation I: Emerging Devices and Technological Concepts, Monday, October 30 at 8:35 a.m. MDT

Abstract Title: TCT 154: Gender Based Assessment of Coronary Artery Disease by Cardiac Phase Tomography Using Machine-Learned Algorithms
Presenter: Dr. Thomas Stuckey, Clinical Professor of Medicine, UNC School of Medicine, Cone Health Heart and Vascular Center Greensboro, NC
Session Title, Date and Time: Monday, October 30 at 10:42 a.m. MDT

Abstract Title: TCT 177: Assessing Coronary Artery Disease by Cardiac Phase Tomography Using Machine-Learned Algorithms in Obese and Elderly Subjects
Presenter: Dr. Thomas Stuckey, Clinical Professor of Medicine, UNC School of Medicine, Cone Health Heart and Vascular Center Greensboro, NC
Session Title, Date and Time: Monday, October 30 at 11:18 a.m. MDT

• Coronary angiography results were paired with cPSTA data from 512 subjects to generate a machine-learned algorithm to assess for significant CAD
• A separate verification cohort of 94 subjects was used to prospectively test the accuracy
• Analyses focused on total verification population and on CADLAD by gender (male vs. female), age (> 65 vs. < 65 years of age), and obesity (BMI of > 30 vs. < 30)
• Results suggest that the cPSTA System performs well overall across the verification population and respective subpopulations

About the Coronary Artery Disease Learning and Algorithm Development (CADLAD) Clinical Study
A two-stage investigational study at 13 sites in the U.S. is currently underway to support CorVista's development and regulatory filings. The ongoing study, with more than 2,000 patients enrolled so far, will develop and measure the performance of a machine-learned algorithm for CAD detection.

About Coronary Artery Disease
Coronary Artery Disease (CAD) is when the heart becomes weakened from a deficient supply of oxygenated blood due to a buildup of plaque in the coronary arteries, which can lead to blood clots, chest pain, and cardiac arrest. CAD affects approximately 15.5 million Americans and is the number one cause of death in the U.S.

About CorVista
CorVista is designed to scan signals emitted by the body with a synchronous array of seven sensors on Analytics 4 Life's proprietary collection device. After the signals are acquired, the signal package is instantaneously transmitted to the cloud where it is analyzed by a machine-learned algorithm to generate a unique image and a heart model indicating areas of potential heart disease associated with the presence of CAD. The results of the test are displayed on a secure physician web portal that, in combination with a patient's medical history, risk factors, and symptoms, are used by the interpreting physician to recommend further treatment. CorVista is an investigational device limited by federal law to investigational use. CorVista is not available for commercial distribution.

About Analytics 4 Life
Analytics 4 Life is pioneering digital health using artificial intelligence to develop a completely new form of medical imaging. With an initial focus on coronary artery disease (CAD), Analytics 4 Life is advancing a novel, radiation-free, and exercise-free cardiac imaging technology aimed at improving existing care pathways. Analytics 4 Life is based in Toronto with U.S. headquarters in Research Triangle Park, NC. For more information, visit www.analytics4life.com. 

SOURCE Analytics 4 Life

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