Cleerly

Cleerly uses AI to analyze coronary CT scans, quantifying plaque and stenosis to characterize heart disease risk with precision beyond visual reading.

Overview

Cleerly transforms coronary CT angiography into quantitative cardiovascular analysis. Its AI algorithms segment and characterize plaque throughout the coronary tree, distinguishing types associated with different risk profiles, and produce reports that give cardiologists a detailed map rather than a qualitative impression. The approach supports earlier identification of at-risk patients and more precise tracking of disease progression or treatment response. Cleerly works with imaging centers and health systems, integrating into existing CT workflows, and its clinical evidence base includes studies correlating its measurements with outcomes. For a disease that remains a leading cause of death globally, the promise is straightforward: better information from scans patients already receive, delivered before events rather than after. The platform’s serial-analysis capability allows comparison across scans over time, making it useful for monitoring how lifestyle changes or therapies affect plaque burden in individual patients. Ongoing clinical studies continue to build the evidence base, and the company works with payers and professional societies to establish quantitative assessment as standard practice.

Key Features

• Automated plaque characterization across the coronary tree
• Quantitative stenosis and burden measurements in clinical reports
• Integration into existing CT angiography workflows
• Serial scan comparison for progression and treatment monitoring
• Clinical evidence correlating measurements with patient outcomes
• Risk-profile differentiation based on plaque type rather than stenosis alone

Pricing

Cleerly contracts with healthcare providers and imaging networks through enterprise agreements, typically priced per analysis. Deployment includes workflow integration and clinical onboarding support.

Who Is It For

Cardiologists, imaging centers, and health systems seeking precision in coronary disease assessment, and preventive-cardiology programs that want objective measures to guide treatment decisions. It fits practices moving toward quantitative imaging where treatment decisions benefit from evidence beyond a radiologist’s visual estimate.