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The Medical Face 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 the potential exposure to blood and body fluids. This is a single use, disposable device(s), provided non-sterile.

The Medical Face Masks are single use, three-layer, flat-pleated style with ear loops and nose piece. The Medical Face Masks are manufactured with three layers, the inner and outer layers are made of spun-bond polypropylene, and the middle layer is made of melt blown polypropylene filter. The ear loops are held in place over the users' mouth and nose by two elastic ear loops welded to the facemask. The elastic ear loops are not made with natural rubber latex. The nose piece in the layers of facemask is to allow the user to fit the facemask around their nose, which is made of Galvanized iron wire+ Polyethylene(PE). The medical face masks will be provided in blue. The medical face masks are sold non-sterile and are intended to be single use, disposable devices.

The provided document is a 510(k) Premarket Notification for a Medical Face Mask, not an AI/ML medical device. Therefore, the information requested about acceptance criteria and study proving an AI/ML device meets them (including sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth establishment, and training set details) is not applicable and cannot be extracted from this document.

The document describes the non-clinical performance testing of a physical medical device (face mask) to demonstrate its substantial equivalence to a predicate device, focusing on material properties, fluid resistance, filtration efficiency, differential pressure, flammability, and biocompatibility.

Here's the relevant information that can be extracted, pertaining to the physical medical face mask, structured to highlight the difference from an AI/ML device study:

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

(Note: The "Reported Device Performance" and "Acceptance Criteria" columns from the document were swapped in this table for logical flow, as the "Result" column in the document appears to be the actual measured performance, and the "Acceptance Criteria" column lists the threshold required for passing.)

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

• Sample Size: For Fluid Resistance Performance, the sample size was 32 (32 out of 32 tested). Sample sizes for other tests (Particulate Filtration Efficiency, Bacterial Filtration Efficiency, Differential Pressure, Flammability, Biocompatibility) are not specified in this document, though these are typically standardized tests with defined sample sizes.
• Data Provenance: Not explicitly stated as "data provenance" in the context of an AI/ML study. However, the manufacturer is CHONGQING CHAOKE INDUSTRY DEVELOPMENT CO.,LTD. in Chongqing, China. The tests are non-clinical (laboratory-based) and are likely performed in a controlled laboratory environment conforming to the specified ASTM and other standards. The document does not specify if the testing was retrospective or prospective, but rather that it was performed for this submission.

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 physical medical device (face mask) undergoing laboratory performance testing against established standards, not an AI/ML algorithm whose performance is measured against expert human-labeled ground truth.

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

• Not Applicable. See point 3. Testing involves standard laboratory procedures and measurements, not human adjudication of AI output.

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. See point 3. This device is not an AI/ML system and does not involve human readers or image interpretation.

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

• Not Applicable. See point 3.

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

• Ground truth in this context refers to established physical/chemical standards and measurement methods (e.g., ASTM F1862 for fluid resistance, ASTM F2299 for particulate filtration, etc.). The device's performance is measured against these technical specifications, not against expert human interpretations or clinical outcomes in the way an AI diagnostic tool would be.

8. The sample size for the training set:

• Not Applicable. This is not an AI/ML device; there is no training set.

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

• Not Applicable. This is not an AI/ML device; there is no training set.

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