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The Protective Face Mask for Medical Use is intended to worn by the healthcare personnel during procedure to protect both the patient and the healthcare personnel from the transfer of microorganisms, body fluids, and particulate material. These face masks are 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/sterile.

The Protective Face Masks for Medical Use are flat-folded masks are three layers of materials consisting of polypropylene spun-bond (outer layer and inner layer), polypropylene melt-blown (middle layers). The masks contain a conformable nose piece enclosed in a binding tape welding the top edge to conform to the contours of the face. In addition, the masks contain an ultrasonically welded, elastic ear loops not made with natural rubber latex, to secure the masks in place on the wearer. The masks are offered in white. The Protective Face Masks for Medical Use are single use, disposable device, provided non-sterile/sterile.

The provided document is a 510(k) premarket notification for a "Protective Face Mask for Medical Use." It assesses the substantial equivalence of the proposed device to predicate devices. The study conducted is a non-clinical performance test, not a study involving human readers or AI.

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

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

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Sample Size:
  • Fluid Resistance Performance: 32 samples (indicated by "32 out of 32 pass")
  • For other tests (Particulate Filtration Efficiency, Bacterial Filtration Efficiency, Differential Pressure, Flammability, Biocompatibility), the exact sample size is not explicitly stated, but the results are reported as meeting the criteria.
• Data Provenance: The tests were conducted to support the 510(k) submission from Hunan Heng Chang Pharmaceutical Co., Ltd. in China. The study is a non-clinical performance test of the device itself, not a study based on patient data, so "retrospective or prospective" and "country of origin of the data" in the typical clinical sense are not applicable. It's a laboratory-based device performance evaluation.

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 a submission for a medical face mask, evaluated against established performance standards (ASTM, EN, ISO, CFR) through physical and biological testing, not through expert interpretation of medical images or diagnoses. Ground truth is defined by the objective measurement criteria of these standards.

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

Not applicable. This document describes laboratory performance testing, not a study involving human interpretation or adjudication. The results are based on objective measurements according to defined test methods.

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 MRMC comparative effectiveness study was done. The submission explicitly states: "No clinical study is included in this submission."

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

This is not applicable, as the device is a physical medical face mask, not an AI algorithm.

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

The "ground truth" for the non-clinical tests is represented by the objective acceptance criteria defined in internationally recognized standards such as ASTM F1862M-17, ASTM F2299, ASTM F2101-19, EN 14683:2019 Annex C, 16 CFR 1610, ISO 10993-5: 2009, and ISO 10993-10: 2010. These standards specify how the device should perform under controlled laboratory conditions.

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. As above, this is a physical device, not an AI model.

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