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Medical Protective Masks is intended to be worn to protect both the patient and healthcare persomel from transfer of microorganisms, body fluids and particulate material. It is intended for use in infection control practices to reduce the potential exposure to blood and body fluids. This is a single use, disposable device(s), provided non sterile.

The mask materials include four layers, the inner and outer layers are made of spun-bond polypropylene, and the two middle layers are melt-blown polypropylene and non-woven polypropylene filters, respectively. The ear straps are held in place over the users' mouth and nose by two elastic ear straps welded to the facemask. The elastic ear straps are not made with natural rubber latex. The nose piece on the layers of facemask is to allow the user to fit the facemask around their nose, which is made of PE (polyethylene) with dual-Galvanized wire. The mask is a single use, disposable device, provided non-sterile in black color.

The provided document (K223686) is a 510(k) Premarket Notification for Medical Protective Masks. This document focuses on demonstrating substantial equivalence to a predicate device, primarily through non-clinical performance testing and material comparisons.

It is crucial to understand that this document describes the regulatory acceptance of a medical mask device, NOT an AI/ML-driven device. Therefore, many of the requested fields regarding AI/ML-specific details (like training/test sets, ground truth establishment for AI, expert adjudication, MRMC studies, and standalone AI performance) are not applicable to this submission. The "study" referenced in the prompt (as proving the device meets acceptance criteria) refers to the non-clinical performance testing of the physical mask, not a clinical or AI-based study.

Here's the information extracted from the document relevant to the performance of the Medical Protective Masks:

1. Table of Acceptance Criteria and Reported Device Performance (Non-Clinical)

Biocompatibility Testing:

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

• Sample Size: Not explicitly stated as a single "test set" size. For performance tests:
  • For Resistance to penetration by synthetic blood: "32 of 32 test articles" were used for each test (Test 1-3).
  • For other non-clinical tests (BFE, Differential Pressure, PFE, Flame Spread), the specific number of masks/samples tested is not explicitly detailed in the provided summary, but the results are reported as ranges or singular outcomes.
• Data Provenance: The document does not specify a country of origin for the non-clinical test data beyond the manufacturer being in China. The data would be from laboratory testing.
• Retrospective or Prospective: Not applicable in the context of mask performance testing. These are laboratory characterizations.

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

• Not applicable. The "ground truth" for physical mask performance is established by recognized international standards (e.g., ASTM F2100-19, ISO 10993 series). These are objective, quantitative measurements performed by accredited laboratories, not subject to expert consensus in the way an AI model's ground truth for medical images would be.

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

• Not applicable. As noted above, these are objective physical and chemical tests, not subjective interpretations requiring adjudication.

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. This is a medical mask, not an AI-assisted diagnostic device. Therefore, no MRMC study or AI assistance evaluation was performed.

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

• Not applicable. This is a physical medical mask, not an algorithm.

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

• The "ground truth" is based on objective, quantitative measurements defined by recognized international standards (e.g., ASTM F2100-19 for performance, ISO 10993 for biocompatibility). For example, Bacterial Filtration Efficiency is measured precisely by an instrument, not by an expert's opinion.

8. The sample size for the training set:

• Not applicable. This is a physical device, not an AI model requiring a training set.

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

• Not applicable, for the same reason as above.

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