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The filter kit is intended for single patient use as an accessory to a small volume nebulizer. The device can be used in a hospital, home, or clinic environment with patients 5 years and above to minimize the amount of medical aerosol exhaled into the air.

The Filtered Mouthpiece is an accessory to the AeroEclipse® BANTM nebulizer. The filtered mouthpiece kit will be sold as a stand-alone filter kit or as part of a kit with the AeroEclipse® BAN™ nebulizer. The replacement filters can be purchased in bulk pack. The device is a tube with a port to a check valve and a filter element. On inhalation the flow is straight through the tube from the nebulizer to the patient with a check valve preventing air from the filter exhaust from backflowing and diluting the delivered aerosol. On exhalation the check valve opens and all flow is directed through a filter to capture exhaled aerosol particles. This minimizes fugitive emissions from therapies such a methacholine challenge test or the delivery of antibiotics. The filter consists of a plastic body, which incorporates a mechanical filter pad (Synthetic Fibers and Foam) and connectors (male / female).

The provided text is an FDA 510(k) summary for a medical device called the "Monaghan Medical Filtered Mouthpiece Kit." This document focuses on demonstrating the substantial equivalence of this device to existing predicate devices based on non-clinical performance testing, rather than clinical performance data or AI/algorithm performance.

Therefore, many of the requested points regarding AI/algorithm acceptance criteria, study methodologies, ground truth establishment, expert adjudication, and MRMC studies are not applicable to this document as they pertain to the evaluation of AI-driven medical devices.

However, I can extract the relevant acceptance criteria and performance data for the physical device based on the provided text.

Here's a breakdown of the requested information based on the provided document:

Acceptance Criteria and Device Performance (Based on Non-Clinical Testing)

1. Table of acceptance criteria and the reported device performance:

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

• Sample Size: The document does not explicitly state the numerical sample sizes for each non-clinical test (e.g., how many filters were tested for efficiency, how many batches for biocompatibility). It describes the types of tests performed.
• Data Provenance: The tests are described as in-vitro (lab-based) or in-vivo (animal studies for biocompatibility, e.g., rabbits, mice, guinea pigs). The document does not specify the country of origin of the data or whether it was retrospective or prospective, though performance testing is by nature prospective for a new device submission.

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

• Not Applicable. For non-clinical performance testing of a physical device, "ground truth" and "experts" in the context of human readability or AI performance are not relevant. The "ground truth" is established by the validated test methodologies and measurement instruments (e.g., filter efficiency testing protocols, chemical analysis for biocompatibility).

4. Adjudication method for the test set:

• Not Applicable. Adjudication methods like 2+1 or 3+1 typically refer to human expert consensus in image interpretation or diagnostic tasks, which is not relevant to non-clinical device performance testing.

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. No MRMC or human-in-the-loop study was conducted or is relevant to this physical device's non-clinical performance evaluation. The document explicitly states: "The determination of substantial equivalence is not based on Clinical Performance data."

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

• Not Applicable. This device is a physical medical accessory, not an AI algorithm.

7. The type of ground truth used:

• For Aerosol Characterization: Ground truth is established by the performance of the predicate device (AeroEclipse® Durable BAN™ nebulizer K080926) using standardized test methods.
• For Biocompatibility: Ground truth is established by the specified ISO 10993 series of standards and their acceptable limits for various biological responses (e.g., no cytotoxicity, no sensitization).
• For Dry Gas Pathway Testing: Ground truth is established by toxicological risk assessments and acceptable exposure limits for VOCs and PM2.5, based on established scientific principles.
• For Filter Efficiency: Ground truth is established by independent laboratory testing using standardized methods for Bacterial Filtration Efficiency (BFE) and Viral Filtration Efficiency (VFE).

8. The sample size for the training set:

• Not Applicable. This is a physical device, and the evaluation is based on non-clinical performance testing, not machine learning model training.

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

• Not Applicable. See point 8.

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