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The Miethke Shunt System is intended to shunt cerebrospinal fluid (CSF) from the lateral ventricles of the brain into the peritoneum.

The Miethke Shunt System miniNAV Valve is used in the treatment of hydrocephalus. The miniNAV Valve is a small cylindrical tube with inlet and outlet connections at opposite ends. A ball-in-cone valve is integrated in the inlet of the device. A spiral spring maintains the opening pressure of the ball-in-cone valve. A sapphire ball ensures the precise closure of the valve. The housing of the miniNAV Valve is manufactured from titanium. The miniNAV Valve is available as a single device as well as in combination as shunt assistants, catheters, connectors, deflectors and reservoirs.

The McLanahan reservoir is a flushing reservoir. It has an integrated occlusion mechanism while flushing in both the proximal and distal directions. The McLanahan reservoir is manufactured of silicone elastomer and titanium.

The provided document is a 510(k) summary for the Miethke Shunt System miniNAV Valve, which is a medical device and not an AI/ML powered device. As such, the typical acceptance criteria and study designs involving AI performance metrics (like those listed in the prompt, e.g., MRMC studies, standalone performance, ground truth establishment by experts, training/test set sizes) are not applicable to this submission.

The document describes performance data related to the device's physical properties and safety in a specific environment (MRI).

Here's a summary of the performance data presented, formatted to align with the prompt's request where applicable, and noting where typical AI/ML criteria do not apply:

Acceptance Criteria and Device Performance Study for Miethke Shunt System miniNAV Valve

The provided 510(k) summary (K110206) focuses on demonstrating the substantial equivalence of the Miethke Shunt System miniNAV Valve to predicate devices, primarily through engineering and safety testing, particularly regarding MRI compatibility. It does not involve AI/ML performance evaluation.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Sample Size: Not specified in terms of number of devices or clinical cases. The testing appears to be primarily bench testing (engineering and MRI compatibility), rather than clinical studies with human subjects that would typically involve a "test set" in the context of AI/ML evaluation.
• Data Provenance: The document does not specify the origin of any "data provenance" as would be relevant for clinical data (e.g., country of origin, retrospective/prospective). The data pertains to laboratory and engineering tests of the device itself.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• This is not applicable as the evaluation is not based on expert-established ground truth for diagnostic or prognostic performance, but rather on objective engineering and MRI compatibility testing according to international standards.

4. Adjudication method for the test set

• This is not applicable. The testing involves adherence to engineering and safety standards, not interpretation by multiple human readers requiring an adjudication method.

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

• This is not applicable. The device is a physical shunt system, not an AI-powered diagnostic or assistive tool.

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

• This is not applicable. The device is a physical medical implant; there is no "algorithm only" performance to evaluate.

7. The type of ground truth used

• The "ground truth" for this device's performance evaluation is established by international and national standards for medical devices (ISO 7197:2000, ISO/TS 10974:2009, ASTM F2119, ASTM F2115, ASTM F2052, ASTM F2213, ASTM F 2503) related to material properties, function, and compatibility (specifically MRI safety). It is not expert consensus, pathology, or outcomes data in the typical sense of AI/ML validation.

8. The sample size for the training set

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

9. How the ground truth for the training set was established

• This is not applicable as there is no training set for an AI/ML algorithm.

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