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The InZone Detachment System is intended for use with all versions of Stryker Neurovascular detachable coils in the embolization of intracranial aneurysms and other vascular malformations of the neuro and peripheral vasculature.

The Stryker Neurovascular InZone Detachment System is a sterile, handheld, single-patient use device designed for use with Stryker Neurovascular Detachable Coils. The device consists of an enclosure with a detachment button, five LED indicator lamps, a funnel inset at its distal end, and a cable connection port. The device comes pre-loaded with two AAAA (1.5 VDC) batteries.

This document, K160096, is a 510(k) premarket notification for the Stryker Neurovascular InZone Detachment System. It concerns modifications to existing firmware and hardware. However, a table of acceptance criteria and reported device performance derived from a study that specifically tested against such criteria is not present in the provided text. The document refers to "Verification Testing," which describes the tests performed and the areas assessed, but it doesn't provide a direct comparison table for acceptance criteria against performance metrics for a specific clinical or diagnostic AI/ML product.

Therefore, I cannot fulfill your request for:

1. A table of acceptance criteria and the reported device performance.
2. Sample size used for the test set and the data provenance.
3. Number of experts used to establish the ground truth for the test set and their qualifications.
4. Adjudication method.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done.
6. If a standalone (algorithm only) performance study was done.
7. The type of ground truth used.
8. The sample size for the training set.
9. How the ground truth for the training set was established.

The provided document is a 510(k) summary for a medical device (a detachment system for coils used in embolization of intracranial aneurysms), not an AI/ML-based diagnostic device. The "Verification Testing" mentioned in section {8} details general engineering and software tests (e.g., detachment cycle time, maximum detachment time, detachment consistency, max voltage/current output, battery detection, fault detection, etc.), and risk assessment, rather than performance metrics related to diagnostic accuracy, sensitivity, or specificity that would typically be associated with AI/ML products and their acceptance criteria in a clinical setting.

The device is a physical system that performs an electrolytic detachment process, and the testing described is engineering verification to ensure the system functions as intended and safely, not a study to evaluate algorithmic performance against a ground truth in a diagnostic context.

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