Stop relying on basic visual stenosis estimates. Let algorithms map, measure, and color-code soft plaque to prevent heart attacks.

Cleerly runs on deep learning algorithms trained on millions of expert-annotated clinical images, deploying advanced boundary segmentation to measure vessel lumens and categorize plaque composition with expert precision. The platform analyzes standard non-invasive CCTA data, distinguishing between stable calcified plaque, intermediate non-calcified plaque, and high-risk low-density soft plaque simultaneously. By converting complex background voxel metrics into an interactive, color-coded dashboard, it standardizes structural cardiac evaluations. For active provider teams, this digital architecture minimizes diagnostic tracking delays completely. When you upload an angiography study, its background matching engine aligns data layers to generate a comprehensive coronary report within an hour, mapping specific lesion locations and ischemia likelihood straight onto your monitor. This automated tracking path provides measurable security, helping clinicians alter therapy directions for over fifty-seven percent of checked candidates during clinical validation trials. This ecosystem matches modern hospital structures by offering native integration modules that drop structured findings directly inside advanced voice reporting interfaces. It minimizes manual transcription errors and click delays across busy departments while creating verifiable data trails to track coronary artery disease changes over time. One honest operational trait to expect is that the software acts as an intensive post-processing analyzer rather than an instant point-of-care preview widget. Because the engine runs complex rule-based calculations and geometric reconstructions to guarantee precision, the standard processing pipeline requires approximately eighty minutes per case file, requiring you to queue studies purposefully around active shift rotations.