QFR (3.0)

The QFR (3.0) is a standalone software package engineered to provide validated and reproducible quantitative assessment of coronary vessels and lesion quantification in patients with coronary artery disease. This is achieved by leveraging deep learning models for accurate 3D reconstruction from 2D angiographic projections, thereby supporting precise clinical decision-making for interventional cardiologists.

A review of the QFR (3.0) device has determined it to be substantially equivalent to other legally marketed devices for its specified clinical applications. This allows the device, an angiographic X-ray system, to be marketed. The QFR (3.0) is intended for use in clinical settings where validated and reproducible quantitative results are necessary to support the assessment of coronary vessels in X-ray angiographic images of individual patients with coronary artery disease, with results intended for use by responsible clinicians.The QFR (3.0) is a standalone software package designed for interventional cardiologists and researchers to obtain quantifications of lesions in coronary vessels. It functions by calculating a QFR value based on a 3D reconstruction derived from 2D angiographic projections, utilizing deep learning models to assist in steps such as vessel selection, contour detection, and determination of specific image frames for analysis. Performance evaluations indicate that these algorithmic improvements meet defined acceptance criteria, while still allowing users to review and correct results. The device shares the same intended use and indications for use as its predicate device, and non-clinical and clinical performance data affirm its comparable performance and safety.

AI Regulatory Concerns

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Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. How does the QFR (3.0)'s deep learning model specifically improve the accuracy and reproducibility of vessel contour detection and QFR value calculation compared to its predicate device?; What are the validated clinical outcomes or benefits observed when QFR (3.0) is used to guide treatment decisions in patients with coronary artery disease, beyond simply providing quantitative results?; Can the QFR (3.0) effectively quantify lesions in complex coronary anatomies or in the presence of calcification, and what are the limitations in such challenging cases?; What is the required expertise or training for clinicians to effectively utilize and interpret the QFR (3.0) results, especially given the allowance for user review and correction?; Are there ongoing or planned studies to compare the diagnostic accuracy and clinical utility of QFR (3.0) against other established physiological assessment methods like FFR or iFR invasively?