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DEEPVESSEL FFR is a coronary physiological simulation software for the clinical quantitative analysis of previously acquired Computed Tomography DICOM data for clinically stable symptomatic patients with coronary artery disease. It provides DVFFR (a CT-derived FFR measurement) computed from static coronary CTA images using deep learning neural networks that encode imaging, structural, and functional characteristics of coronary arteries through learning. DEEPVESSEL FFR analysis is intended to support the functional evaluation of coronary artery disease. The results of the analysis are provided to support qualified clinicians to aid in the evaluation and assessment of coronary arteries. DEEPVESSEL FFR results are intended to be used by qualified clinicians in conunction with the patient's clinical history, symptoms, and other diagnostic tests, as well as the clinician's professional judgment.

DEEPVESSEL FFR is a coronary physiological simulation software for the clinical quantitative and qualitative analysis of previously acquired Computed Tomography DICOM data for clinically stable symptomatic patients with coronary artery disease. It estimates FFR values from static coronary CTA images with extracted coronary tree structures using deep learning neural networks. DEEPVESSEL FFR analysis is intended to support the functional evaluation of CAD. The software processes these images semi-automatically, and it generates a 3D model of the coronay artery tree and computes DVFFR (CT-derived FFR) values. Qualified image analysts interact with the software by providing manual edits to the 3D coronary artery tree segmentations when needed, and oversees outputs along the processing steps. DVFFR analysis results are sent electronically to the physicians via a third-party service portal application. DVFFR software is independent of imaging equipment, imaging protocols and equipment vendors; the clinical validation study report includes the specific imaging scanner types and imaging acquisition parameters used in the clinical validation of the product.