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The Disposable Medical Face Masks are intended to be worn to protect both the patient and healthcare personnel from transfer of microorganisms, body fluids and particulate material. These mask 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, provided nonsterile.

The proposed devices are single use, three-layer, flat masks with ear-loops and nose piece. The Disposable Medical Face Masks are manufactured with three layers, the outer layer is made of PPSB non-woven fabric, which the chemical composition is the mixture of Polypropylene and color master batch. Inner layer is made of Polyethylene, polypropylene and mixture of fiber finishes. And the middle layer is made of melt blown polypropylene filter. The model of proposed device ,YX001, is held in place over the user's mouth and nose bu two elastic ear loops welded to the face mask. The elastic ear loops are not made with natural rubber latex. The nose piece contained in the proposed device is in the layers of face mask to allow the user to fit the face mask around their nose, which is made of Malleable aluminum wire. The proposed devices are sold non-sterile and are intended to be single use, disposable devices. The colorants used for mask are Copper phthalocyanine and Titanium dioxide.

This document does not describe an AI medical device study. It is a 510(k) summary for a Disposable Medical Face Mask (K202439), demonstrating substantial equivalence to a predicate device (K153496).

Therefore, I cannot provide information on:

• Acceptance criteria and device performance for an AI/ML model
• Sample size for a test set or data provenance in the context of AI
• Number of experts or their qualifications for ground truth in AI
• Adjudication methods for AI test sets
• MRMC studies or effect sizes for AI assistance
• Standalone AI performance
• Training set details for AI
• How ground truth for a training set was established for AI

The document focuses on non-clinical performance characteristics of the medical face mask, such as:

• Resistance to penetration by synthetic blood
• Sub-micron particulate filtration efficiency (PFE)
• Bacterial Filtration Efficiency (BFE)
• Differential Pressure (breathability)
• Flame spread
• Biocompatibility (Cytotoxicity, Irritation, Sensitization)

The acceptance criteria and performance data are entirely related to these physical and biological characteristics of a face mask, not an AI/ML algorithm.

Here is the relevant information from the document as it pertains to the face mask, not an AI device:

1. Table of Acceptance Criteria and Reported Device Performance (for the face mask):

2. Sample Size Used for the Test Set and the Data Provenance:
This document does not specify general "test sets" in the AI/ML sense. For the performance tests (e.g., BFE, PFE), specific sample sizes for each individual test are not explicitly stated in this summary, but these are standard laboratory tests typically performed on a defined number of mask samples as per the respective ASTM/ISO standards.
The data provenance is from Hunan EEXI Technology & Service Co.,Ltd. in China, as they are the submitter (K202439) and presumably conducted these tests on their own product. The tests are non-clinical, essentially lab-based performance evaluations of the physical product, not data-driven AI evaluations.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts:
Not applicable. Ground truth as typically understood in AI/ML (e.g., expert labels on medical images) is not relevant here. The "ground truth" for a face mask's physical properties are the specific, measurable outcomes of the standardized ASTM/ISO tests. These are determined by lab measurements, not expert consensus in the diagnostic sense.

4. Adjudication Method for the Test Set:
Not applicable. There is no adjudication process described for these performance tests as they are objective quantitative or qualitative (pass/fail) lab measurements based on established standards.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:
No, this is not an AI device. An MRMC study is not applicable for a physical product like a face mask.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:
Not applicable. This is not an AI device.

7. The Type of Ground Truth Used:
The "ground truth" for the face mask's performance is established by objective laboratory measurements performed according to recognized international standards (ASTM, ISO, etc.). For example:

• Fluid resistance: Measured penetration at specific pressure.
• Filtration efficiency: Measured percentage of particles/bacteria filtered.
• Differential pressure: Measured pressure drop.
• Biocompatibility: Lab test results indicating cellular response (cytotoxicity), skin reactions (irritation), or immune response (sensitization).

8. The Sample Size for the Training Set:
Not applicable. This is not an AI device. There is no "training set."

9. How the Ground Truth for the Training Set Was Established:
Not applicable. As there is no training set for an AI model, there is no ground truth established for one.

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