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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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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.

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 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 text describes the Volcano CORE M2 Vascular System, an ultrasonic pulsed echo imaging system. The performance data section outlines the design verification, software verification and validation, simulated use/usability validation, and image validation conducted to support the substantial equivalence claim to a predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify exact sample sizes for test sets in most categories.

• Design Verification: Details are general about testing performed on "the CORE M2 system" or "each of its subassemblies" or "each of the CORE M2 System packages." Specific numbers of units tested are not provided.
• Software Verification and Validation: Mentions "Unit, integration, and system level software testing was conducted on the CORE M2 v4.1 software." No specific number of test cases or cycles is provided.
• Simulated Use / Usability Validation: The text implies testing with users ("demonstrates that users are able to use the device safely and effectively"), but does not state the number of users or scenarios.
• Image Validation: The text states "An Image Validation was also conducted to confirm that the images acquired and displayed by the CORE M2 System are clinically acceptable for end users." No specific number of images or patients for this validation is provided.

Data Provenance: Not explicitly stated (e.g., country of origin). The testing seems to be internal verification by Volcano Corporation rather than external clinical data. The studies described are likely prospective in nature, as they involve testing the device against predefined specifications.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

The document does not provide details on the number or qualifications of experts specifically used to establish ground truth for the test set in the same way one might describe a clinical study comparing diagnostic accuracy.

• For Image Validation, it mentions "clinically acceptable for end users," implying clinical judgment, but does not specify how many clinicians or what their qualifications were.

4. Adjudication Method for the Test Set

The document does not describe a formal adjudication method (e.g., 2+1, 3+1) for any of the performance data tests. The evaluations described are primarily engineering and software verification against specifications, or general statements about clinical acceptability.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Its Effect Size

No, the document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. The performance data focuses on validating the device's technical specifications and image quality, not its impact on human reader performance with or without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Study Was Done

The device itself is an "Ultrasonic Pulsed Echo Imaging System" which provides images for a human to interpret. Therefore, the concept of a "standalone" algorithmic performance (without human-in-the-loop) in the context of an imaging system's primary output is not directly applicable in the way it would be for an AI diagnostic algorithm. The system itself (hardware + software for image generation) is being evaluated for its standalone ability to acquire and display clinically acceptable images. The section "Image Validation" could be interpreted as evaluating the standalone image quality generated by the system.

7. The Type of Ground Truth Used

• Design Verification: Ground truth for these tests (electrical safety, EMC, environmental, packaging, hardware) would be the engineering specifications and industry standards (e.g., ANSI/AAMI, IEC, ASTM) that the device must meet.
• Software Verification and Validation: Ground truth would be the defined software requirements.
• Simulated Use / Usability Validation: Ground truth would be user needs and safety requirements.
• Image Validation: Ground truth appears to be expert consensus on "clinical acceptability" of the images, although the specifics are not detailed.

8. The Sample Size for the Training Set

The document does not describe any machine learning components that would require a distinct "training set." The CORE M2 Vascular System is an ultrasonic imaging system, and its development and validation are described in terms of engineering, software, and image quality verification, not in terms of training an AI model on a dataset.

9. How the Ground Truth for the Training Set Was Established

As no training set is mentioned in the context of machine learning, this question is not applicable.

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