Cleerly LABS (v2.0)

Cleerly LABS (v2.0) is a web-based software tool for medical professionals to analyze cardiac CT images and assess coronary artery disease. It uses machine learning to identify and segment coronary arteries, allowing clinicians to measure plaque, stenosis, and vessel characteristics to support treatment decisions. The software post-processes CT images from any compatible scanner and outputs findings in an interactive CORONARY Report.

The subject device maintains identical platform architecture (client-server Google Chrome application), image input format (DICOM 3.0+), and core analysis features as the predicate, including 2D/3D imaging, segmentation, measurements, and stenosis marking. The primary change is an administrative product code shift from LLZ to QIH (with LLZ as subsequent code) to reflect the device's use of non-adaptive machine learning algorithms. Minor workflow enhancements and labeling modifications were introduced, but no changes were made to underlying algorithms or mathematical calculations.

The device was designed and validated per 21 CFR Part 820.30 (Design Controls), ISO 14971:2019-12 (Risk Management), AAMI TIR 57:2016 (Medical Device Security), DICOM standards, and ANSI AAMI IEC 62304:2005/A1:2016 (Medical Device Software Life Cycle Processes). Software evaluation activities included testing in pre-production environments and production release verification with regression testing to confirm changes did not affect overall performance.

The subject device is substantially equivalent because it has identical intended use and indications for use as the predicate K202280, uses the same software algorithms and fundamental technology design, and introduces only modifications to labeling and workflow that do not alter core segmentation or calculation logic. Software testing confirmed the device continues to perform as intended with no changes to machine learning algorithms or mathematical equations, and regression testing demonstrated that technological enhancements did not affect safety or effectiveness for the same clinical application.