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The inflation device is designed to inflate and deflate an angioplasty balloon or other interventional device and to monitor pressure within the balloon.

An inflation device is designed to generate and monitor pressure and is used in angioplasty or other interventional procedures. It is a sterile hand-held mechanical device comprised of a 20 cc syringe, pressure gauge, plunger, connector tube and a male luer fitting for connection to a catheter. The device may be used in conjunction with other accessories such as a hemostatic valve or stopcock, guide wire introducer or torque device.

Vanguard receives previously used inflation devices from healthcare facilities; cleans, inspects, tests, repackages and sterilizes the devices; and returns them to the healthcare facility.

The provided 510(k) summary for K012480 describes the reprocessed Vanguard Inflation Devices. This submission focuses on demonstrating substantial equivalence to predicate devices, rather than establishing efficacy of a novel device or an AI algorithm. Therefore, many of the requested elements for AI/algorithm performance studies are not applicable.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not explicitly state quantitative acceptance criteria in numerical terms that would be typical for an AI/algorithm performance study (e.g., sensitivity, specificity, AUC thresholds). Instead, the acceptance criteria are implicitly defined by the demonstration that the reprocessed devices perform "as intended" and are "safe and effective" and "essentially identical" to the OEM predicate devices.

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

The document does not specify a distinct "test set" in the context of an AI/algorithm study. The testing described involves "functional/performance," "biocompatibility," "sterilization," and "packaging validations." It does not provide sample sizes for these tests, nor does it detail the provenance (country of origin, retrospective/prospective) of any data beyond stating that Vanguard "receives previously used inflation devices from healthcare facilities." This implies the devices themselves are from real-world usage, but the specific data used for testing is not detailed in terms of its origin.

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

This is not applicable as the submission is not for an AI/algorithm that requires expert-established ground truth on diagnostic images or other clinical data. The "ground truth" here is the functional performance and safety of a medical device, evaluated through engineering and biological testing against established standards or predicate device performance.

4. Adjudication Method for the Test Set

Not applicable. The testing described (functional, biocompatibility, sterilization, packaging) would follow standard laboratory and engineering protocols, not clinical adjudication methods used for interpreting images or clinical data.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, this type of study was not done. MRMC studies are typically for evaluating the impact of an AI algorithm on human reader performance, which is not the subject of this 510(k) submission.

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

Not applicable. This submission is about a reprocessed physical medical device, not an AI algorithm.

7. The Type of Ground Truth Used

The "ground truth" in this context refers to the acceptable performance and safety profiles of inflation devices. This is established through:

• Predicate Device Performance: The OEM devices' known performance and safety.
• Engineering Standards: Bench testing using established methods to verify pressure generation, sealing, material compatibility, etc.
• Biological Testing: Biocompatibility tests to ensure the reprocessed materials are safe for patient contact.
• Sterilization Validation: Demonstrating that the sterilization process renders the devices sterile.

8. The Sample Size for the Training Set

Not applicable. There is no AI algorithm being trained, so no training set is involved.

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

Not applicable. There is no AI algorithm, and therefore no training set or ground truth for it.

In summary:

This 510(k) pertains to the substantial equivalence of reprocessed medical devices, not the performance of an AI algorithm. Therefore, the requested information related to AI study design elements (like sample sizes for test/training, expert adjudication, MRMC studies, and standalone performance) is not available or relevant in this document. The study described focuses on engineering, sterilization, packaging, and biocompatibility testing to ensure the reprocessed devices perform identically to and are as safe and effective as their original counterparts.

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