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The Medical Surgical Masks-Non Sterile is intended to be worn to protect and healthcare personnel from the transfer of microorganisms, body fluids, and particulate material. The Medical Surgical Masks-Non Sterile 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 Medical Surgical Masks-Non Sterile is intended to be worn to protect both the patient and healthcare personnel from the transfer of microorganisms, body fluids, and particulate material. The Medical Surgical Masks-Non Sterile 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 proposed device(s) are White color, and Flat Pleated type mask, utilizing Ear Loops' way for wearing, and they all have Nose Piece design for fitting the facemask around the nose.

The proposed device(s) are manufactured with three layers, the inner and outer layers are made of polypropylene non-woven fabric, and the middle layer is made of polypropylene melt-blown fabric.

The Medical Surgical Masks-Non Sterile is held in place over the user's mouth and nose by two elastic ear loops welded to the facemask. The elastic ear loops are made with 30% polyamide and 70% polyurethane.

The nose piece contained in the proposed device(s) is in the layers of the facemask to allow the user to fit the facemask around their nose, which is made of polypropylene and iron wire.

The proposed device(s) are sold non-sterile and are intended to be single-use, disposable devices.

The provided text describes the acceptance criteria and performance of a Medical Surgical Mask-Non Sterile. It is a regulatory submission to the FDA, demonstrating substantial equivalence to a predicate device, rather than a study proving the device meets criteria in an AI/algorithm context. Therefore, many of the requested points, such as sample size for test/training sets, data provenance, number of experts, adjudication methods, MRMC studies, or specific AI-related ground truths, are not applicable to this document.

However, I can extract the relevant information regarding the acceptance criteria and the device's performance based on the non-clinical tests conducted for this medical device.

Acceptance Criteria and Reported Device Performance

Detailed breakdown of requested information:

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

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

   • The document primarily describes non-clinical bench testing for a medical surgical mask. It does not refer to "test sets" in the context of AI/algorithm evaluation but rather samples of the physical medical device used for specific performance tests.
   • Sample Size: Not explicitly stated for each individual test. Standard medical device testing protocols typically specify the number of units to be tested per batch or for regulatory submission, but these details are not provided in this summary.
   • Data Provenance: The tests were performed to demonstrate compliance with international standards (ISO, ASTM, MIL-M-36945C) and US regulations (16 CFR 1610). The manufacturer is SHANDONG T&F NONWOVEN CO., LTD. located in CHINA. The testing was conducted in direct support of their 510(k) submission, implying it's "prospective" in terms of its regulatory purpose, but the specific conduct of the tests themselves would follow standard laboratory practices.

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 document pertains to physical device testing against established performance standards, not diagnostic accuracy requiring expert panel consensus/ground truth. The "ground truth" here is the pass/fail criteria defined by the recognized standards (e.g., ASTM F2100, ISO 10993).

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

   • Not Applicable. As per point 3, there's no diagnostic task or expert review 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:

   • Not Applicable. This document is about a physical medical device (surgical mask), not an AI algorithm.

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

   • Not Applicable. This document is about a physical medical device (surgical mask), not an AI algorithm.

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

   • The "ground truth" for this device's performance is defined by established international and national standards and regulations (e.g., ISO 10993, ASTM F2100, 16 CFR 1610, MIL-M-36945C). The device's performance is measured against the quantitative and qualitative requirements set forth in these standards. For biocompatibility, it's the absence of cytotoxic, irritant, or sensitizing effects as per the ISO standards. For filtration, it's achieving specific percentage thresholds.

8. The sample size for the training set:

   • Not Applicable. There is no "training set" as this is not an AI algorithm.

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

   • Not Applicable. There is no "training set."

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