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The Honeywell Procedure Mask is intended to be worn to protect both the patient and healthcare personnel from transfer of microorganisms, body fluids, and particulate material. These face masks are intended for use in infection control practices to reduce potential exposure to blood and body fluids. This is a single use, disposable device, provided non-sterile.

The Honeywell Procedure Mask is composed of three layers that are a flat, pleated style mask with earloops to secure it over the users' mouth and face. The inner and outer layers are manufactured from spun-bond polypropylene. The middle layer is made of melt-blown polypropylene. The mask is a single use, disposable device, provided nonsterile and is not made from natural rubber latex.

The provided text is a 510(k) Premarket Notification for a medical device, specifically the "Honeywell Procedure Mask". It describes the device, its intended use, comparison to a predicate device, and the non-clinical testing performed to establish substantial equivalence.

However, it's crucial to understand that this document does not describe a study involving an AI/Machine Learning device or a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. The "acceptance criteria" and "study that proves the device meets the acceptance criteria" in this document refer to the physical and performance characteristics of a surgical mask, not the performance of an AI algorithm in a diagnostic imaging context.

Therefore, many of the requested points related to AI/MRMC studies, such as the number of experts, adjudication methods, effect size, and ground truth establishment for AI training/test sets, are not applicable to this submission.

I will interpret the request in the context of the provided document, focusing on the acceptance criteria and performance testing for the surgical mask device.

Here's the information extracted from the document:

1. A table of acceptance criteria and the reported device performance:

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

• Sample Size: For performance testing (ASTM F1862, F2299, F2101, EN 14683 Annex C, 16 CFR Part 1610), 32 samples in each of 3 different, non-consecutive lots were used. Biocompatibility testing typically uses a smaller number of samples as per the specific ISO guidance.
• Data Provenance: The document does not specify the country of origin of the data or whether the tests were retrospective or prospective beyond the standard laboratory testing procedures. It is implied these are prospective laboratory tests conducted to meet regulatory requirements.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

• Not Applicable. This is not an AI/ML study involving human interpretation of data where "experts" establish ground truth. The "ground truth" here is established through standardized laboratory test results and measurements by trained technicians using calibrated equipment according to specified ASTM, EN, and ISO standards.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• Not Applicable. As this is a physical device testing, not an AI diagnostic study, there is no need for adjudication methods for interpretations. The results are obtained from objective measurements.

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:

• No. An MRMC comparative effectiveness study was not conducted. This is a 510(k) submission for a physical medical device (surgical mask), not an AI-assisted diagnostic tool.

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

• Not Applicable. This submission is for a physical medical device, not a standalone algorithm.

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

• For the purposes of this submission, the "ground truth" for the device's performance is established by objective measurements obtained through standardized laboratory test methodologies (e.g., precise measurement of fluid penetration at a given pressure, filtration efficiency percentage, differential pressure measurement). Biocompatibility is determined by standardized in-vitro or in-vivo biological tests according to ISO 10993 standards.

8. The sample size for the training set:

• Not Applicable. There is no "training set" in the context of this traditional medical device submission, as it does not involve AI or machine learning. The device's characteristics are determined by its design and material properties, validated by testing.

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

• Not Applicable. As there is no training set for an AI/ML model, this question is not relevant to the provided document.

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