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The Surgical 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 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, provided non-sterile.

The proposed devices are single use, three-layer, flat masks with straps and nose piece. The Surgical 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 model of proposed device, YX011, is held in place over the user's mouth and nose by two elastic ear loops welded to the face mask. The elastic ear loops are not made with natural rubber latex.

The model of proposed device, YX121, is held in place over the user's mouth and nose by four ties welded to the face mask. The tie is made of spun-bond polypropylene.

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 polyethylene and iron.

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

This document is a 510(k) summary for a "Surgical Face Mask" (Trade Name) from Hunan EEXI Technology & Service Co., Ltd. It's a regulatory submission to the FDA to demonstrate substantial equivalence to a legally marketed predicate device.

Therefore, this document does not describe the acceptance criteria and the study that proves an AI/algorithm-based device meets acceptance criteria. It discusses the performance criteria for a physical medical device (a surgical face mask).

The request asks about aspects of an AI/algorithm study (e.g., sample size for training/test sets, expert adjudication, MRMC studies, ground truth establishment). These concepts are not applicable to the evaluation of a physical surgical face mask as described in this 510(k) submission.

However, I can extract the acceptance criteria and reported device performance for the Surgical Face Mask based on the provided text, and explain why other aspects of your question are not relevant here.

Acceptance Criteria and Reported Device Performance (for the Surgical Face Mask)

The acceptance criteria for this medical device are based on the ASTM F2100-19 standard for the "Performance of Materials Used in Medical Face Masks" and ISO 10993-1 for biocompatibility. The device is intended to meet Level 3 performance based on ASTM F2100.

Here's a table summarizing the performance standards (acceptance criteria) and the reported performance of the subject device (Surgical Face Mask, K202161) and comparison to its predicate device (K153496):

Table of Acceptance Criteria and Reported Device Performance

Response to AI/Algorithm-Specific Questions (Not Applicable to this document)

The following points are pertinent to AI/algorithm-based devices but are not discussed or applicable in this 510(k) submission for a physical surgical face mask. This document focuses on material properties, physical dimensions, and established performance standards for protective apparel.

1. Sample sizes used for the test set and the data provenance: Not applicable. Tests are conducted on physical samples of the face mask, not a 'test set' of data in the AI sense.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Performance is measured objectively through standardized laboratory tests (e.g., measuring particulate filtration). Ground truth is established by the test method's result, not expert consensus.
3. Adjudication method: Not applicable. No human interpretation or adjudication as in AI model evaluation.
4. If a multi reader multi case (MRMC) comparative effectiveness study was done: Not applicable. This is for AI-assisted human reading, not for a physical mask.
5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable. No algorithm is involved.
6. The type of ground truth used: For physical masks, "ground truth" is typically defined by the results of the standardized physical and material tests (e.g., a certain percentage of particles were blocked, a certain fluid penetration resistance was achieved).
7. The sample size for the training set: Not applicable. There is no 'training set' as this is not an AI/ML device.
8. How the ground truth for the training set was established: Not applicable.

In summary, the provided document is a regulatory submission for a physical medical device (surgical face mask) and its evaluation against established performance standards for such products. It does not pertain to the development or validation of an AI/algorithm-based medical device.

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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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