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The Barricade Coil System (BCS) is intended for the endovascular embolization of intracranial aneurysms and other neurovascular abnormalities such as arteriovenous malformations and arteriovenous fistulae. The BCS is also intended for vascular occlusion of blood vessels within the neurovascular system to permanently obstruct blood flow to an aneurysm or other vascular malformation and for arterial and venous embolizations in the peripheral vasculature.

The Barricade Embolization Coil System (BCS) is a series specialized coils that are inserted into the vasculature under angiographic visualization to embolize intracranial aneurysms and other vascular anomalies. The system consists of an embolization coil implant comprised of platinum/tungsten, affixed to a delivery pusher with an introducer sheath to facilitate insertion into the hub of a microcatheter. The system is available in various shapes, lengths and sizes. The devices are to be placed into aneurysms to create blood stasis, reducing flow into the aneurysm and thrombosing the aneurysm. Upon positioning coils into the aneurysm, the coils are electrolytically detached from the delivery pusher in serial manner until the aneurysm is occluded.

This document is a 510(k) premarket notification for the Barricade Embolization Coil System. It does not describe an AI/ML powered medical device, but rather a traditional medical device (embolization coil system). Therefore, most of the requested information regarding AI/ML device performance and study design (sample sizes for test and training sets, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth types) is not applicable or cannot be extracted from this document.

However, I can provide the acceptance criteria and a summary of the study performed based on the provided text, as it relates to the substantial equivalence determination for this particular medical device.

1. Table of Acceptance Criteria and Reported Device Performance

Study Proving Device Meets Acceptance Criteria:

The study was a series of verification and validation tests comparing the modified Barricade Embolization Coil System to its predicate devices (K134482, K131475, K123338, K140104). The goal was to assure substantial equivalence and demonstrate that the device performs as intended.

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

• Sample Size for Test Set: The document states that "All testing was performed on test units representative of finished devices," but does not explicitly provide the specific number of units or data points used for each test.
• Data Provenance: The data is generated from laboratory testing ("verification and validation testing") on the device itself. This is not clinical data from patients (e.g., country of origin is not applicable in this context). The testing is prospective for the purpose of this 510(k) submission.

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

• This is not applicable as the tests performed are primarily engineering and simulated use performance tests on the physical device, not interpretative tasks requiring expert ground truth (e.g., radiology interpretation).

4. Adjudication Method for the Test Set:

• Not applicable for the types of engineering and simulated use performance tests described. The results are objective measurements against defined specifications.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• Not applicable. This is not an AI/ML device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• Not applicable. This is not an AI/ML device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• The "ground truth" for these performance tests is based on pre-defined engineering specifications and performance standards. For example, a tensile strength test would have a specific range of acceptable values, or a simulated use test would have defined operational outcomes (e.g., successful deployment, detachment). The comparison is also made against the performance of predicate devices.

8. The Sample Size for the Training Set:

• Not applicable. This is not an AI/ML device, so there is no "training set" in the context of machine learning.

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

• Not applicable. As above, this is not an AI/ML device.

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