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The Gatekeeper Balloon Catheter is indicated for temporary occlusion of vessels in the peripheral vasculature.

The Gatekeeper Balloon Catheter is an over the wire (OTW) triple-lumen catheter with 2 compliant proximal and distal polyurethane balloons having a maximum diameter of 15 mm and 10 mm respectively. The 2 balloon lumens are connected to extension lines with stopcocks and are used to inflate and deflate the proximal and distal balloons. The third lumen is a guidewire lumen. The distance between the proximal and distal balloons is 2 cm. Two (2) radiopaque marker bands are located within the balloons at each end (30mm apart at the proximal balloon and 15mm apart at the distal balloon) to facilitate balloon placement prior to inflation. The catheter can accommodate a 0.014" diameter (or smaller) guidewire and is compatible with 6Fr (or larger) introducer sheath. The device is a single use, sterile device.

The provided text is a 510(k) summary for the Arch Catheter, LLC's Gatekeeper Balloon Catheter. This device is a medical device, not an AI/ML powered device, so many of the requested categories (such as sample sizes for test and training sets, number of experts, adjudication methods, MRMC studies, or specific ground truths related to diagnostic accuracy) are not applicable.

However, I can extract information related to the device's acceptance criteria and the studies performed to demonstrate its performance against those criteria.

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly present a table of acceptance criteria with corresponding performance values in a single, clear format. Instead, it lists the types of tests performed to demonstrate compliance with recognized standards. The "reported device performance" is implied to meet the standards' requirements, as the conclusion states the device is substantially equivalent based on non-clinical testing.

Here's a summary derived from the text:

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

• Sample Size: Not explicitly stated for each test. The document refers to "non-clinical testing," which typically involves engineering benchtop tests on a representative number of devices, rather than a "test set" in the context of clinical AI/ML studies.
• Data Provenance: Not applicable in the context of clinical data provenance (e.g., country of origin, retrospective/prospective) as the studies were non-clinical benchtop tests.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

• Number of Experts: Not applicable. These are engineering and performance tests, not clinical evaluations requiring expert interpretation for ground truth.
• Qualifications of Experts: Not applicable.

4. Adjudication Method:

• Not applicable as this is not a clinical study involving human assessment of an output, but rather physical performance testing.

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

• No, a MRMC comparative effectiveness study was not done. This is a physical medical device, not an AI-powered diagnostic or assistive tool for human readers. Therefore, there's no concept of human readers improving with or without AI assistance.

6. Standalone (Algorithm only without human-in-the-loop performance) Study:

• Not applicable. This is a physical medical device; there is no "algorithm" or standalone AI performance to evaluate.

7. Type of Ground Truth Used:

• The "ground truth" for these tests is based on the requirements and specifications outlined in recognized international standards (ISO 10555-1, ISO 10555-4) and internal device specifications. For example, "Freedom from Leakage" would have a defined leakage rate that the device must not exceed. "Biocompatibility" is assessed against the criteria of ISO 10993-1. These are objective engineering criteria rather than clinical expert consensus or pathology.

8. Sample Size for the Training Set:

• Not applicable. This is not an AI/ML device that requires a training set.

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

• Not applicable as there is no training set for an AI/ML model.

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