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The CORE M2 Vascular System is used for the qualitative evaluation of vascular morphology in the coronary arteries and vessels of the peripheral vasculature. It is also indicated as an adjunct to conventional angiographic procedures to provide an image of vessel lumen and wall structures.

ChromaFlo is indicated for qualitative blood flow information from peripheral and coronary vasculature; flow information can be an adjunct to other methods of estimating blood flow and blood perfusion.

The CORE M2 Vascular System is used for the qualitative and quantitative evaluation of vascular morphology in the coronary arteries and vessels of the peripheral vasculature. It is also indicated as an adjunct to conventional angiographic procedures to provide an image of vessel lumen and wall structures. The system utilizes the acoustic impedance of vascular structures to provide cross sectional images from inside the vessel. The IVUS catheter uses a transducer near the distal tip to emit and receive high frequency sound waves. The system is then able to analyze the signal that is received by the transducer to differentiate between vessel structures to produce a 360° cross sectional grayscale image.

In addition to producing grayscale IVUS images, the CORE M2 System with Software v4.2 provides the ChromaFlo feature which can be used to identify blood flow. The ChromaFlo feature uses patented technology to provide a visual depiction of blood flow through the vessel. This is accomplished by overlaying a two-dimensional color mapping of relative blood flow velocity on to the grayscale ultrasound image.

The CORE M2 Vascular System consists of a cart mounted touchscreen PC Console, a patient interface module (PIM) for connecting the IVUS Imaging Catheter to the PC Console, and an optional Control Console as an alternative to the touchscreen for control of the CORE M2 System.

The provided document, a 510(k) summary for the CORE M2 Vascular System Software v4.2, describes several verification and validation activities. However, it does not include detailed acceptance criteria or a specific study that quantifies device performance against those criteria in the typical format of a clinical or analytical performance study with metrics like sensitivity, specificity, or accuracy.

Instead, the document focuses on demonstrating that the software modifications maintain substantial equivalence to a predicate device. The performance data presented are primarily engineering and system-level validations, as well as qualitative assessments of clinical acceptability.

Here's an analysis based on the provided text, addressing your points where information is available:

1. Table of Acceptance Criteria and Reported Device Performance

As noted, the document doesn't provide a quantitative table of acceptance criteria and reported device performance in terms of clinical metrics (e.g., sensitivity, specificity, accuracy) for an AI/algorithm. Instead, the performance data focuses on system functionality and image quality.

2. Sample Size for Test Set and Data Provenance

• Test Set Sample Size: Not specified for any of the validation steps. The document refers to "testing" without providing numerical sample sizes for cases, images, or subjects.
• Data Provenance: Not specified. It's unclear if the data used for image validation or acoustic output comparison was from a specific country or if it was retrospective or prospective. Given the nature of a 510(k) for software updates to an existing system, it's likely previous data and newly acquired bench/phantom data were used.

3. Number of Experts and their Qualifications for Ground Truth

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The document mentions "clinically acceptable for end users" in the Image Validation section, implying clinical assessment, but the details of who performed this or their qualifications are absent.

4. Adjudication Method for the Test Set

Not specified.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with AI vs. without AI assistance was not performed or reported in this summary. The device, the "CORE M2 Vascular System Software v4.2," including its "ChromaFlo" feature for qualitative blood flow information, is not presented as an AI-assisted diagnostic tool that augments human interpretation in a comparative effectiveness setting. It's a system for image acquisition and display, with ChromaFlo providing a visual depiction of blood flow.

6. Standalone (Algorithm Only) Performance Study

No. The document describes system-level validations and image quality assessments, but not a standalone performance study of an AI algorithm in the typical sense (e.g., measuring its diagnostic accuracy independently). The ChromaFlo feature is part of the system and provides "qualitative blood flow information" as an "adjunct to other methods," suggesting it's not intended for standalone diagnostic interpretation.

7. Type of Ground Truth Used

The concept of "ground truth" as typically applied to AI performance (e.g., pathology, outcomes data) is not directly addressed in this document. The validations are more about:

• Engineering specifications ("meets specifications").
• Equivalence to a predicate device ("substantially equivalent").
• Meeting user needs ("meets user needs").
• Clinical acceptability ("clinically acceptable for end users").

For the "Image Validation" and "Simulated Use/Usability Validation," the "ground truth" would be the subjective judgment of clinical acceptability and usability by qualified individuals (though not specified). For acoustic output, it would be measured physical properties compared to established standards or the predicate.

8. Sample Size for the Training Set

Not applicable/Not specified. This document is about software modifications to an existing device, including the addition of a feature (ChromaFlo). It does not describe the development or training of a de novo AI algorithm with a distinct training set. The ChromaFlo feature uses "patented technology to provide a visual depiction of blood flow velocity," implying a predefined method rather than a machine learning model requiring a training dataset.

9. How Ground Truth for the Training Set Was Established

Not applicable, as no training set for an AI algorithm appears to have been used in the context of this 510(k) submission for software updates.

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