Coronary Artery Disease Learning and Algorithm Development (CADLAD) Clinical Study

At Analytics 4 Life®, we are committed to developing a revolutionary form of cardiac imaging for CAD by machine learning from using paired physiologic data and gold standard results, all completely source-verified from participating sites.

A two-stage study is being conducted to develop and compare the performance of a machine-learned algorithm to detect cardiac health affected by the presence of CAD by pairing a patient’s physiologic signals with their cardiac catheterization results.

A clinical diagnosis of significant coronary artery disease is defined as either a ≥ 70% stenosis or a reduced fractional flow rate of ≤ 0.80 in the major coronary arteries and their distributions, including the left main artery (LMA), left anterior descending artery (LAD), circumflex artery (LCX), and the right coronary artery (RCA).

Stage 1:
Machine
Learning

Prospective, non-randomized trial to develop a machine-learned algorithm to detect and assess significant CAD

Stage 2:
Validation

Prospective, blinded, non-randomized, paired comparison trial to test the machine-learned algorithm

Caution—Investigational Device. Limited by Federal Law to Investigational Use. CorVista® is not available for commercial distribution.