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The Surgical Masks are 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 Surgical Masks are Flat Pleated type mask, utilizing Ear Loops way for wearing, and they all have Nose Piece design for fitting the face mask around the nose. The Surgical Masks are manufactured with three/four layers. The surgical mask has two models which are SNN200640 and MN12. They are basically the same, the only difference is the SNN200640 has the three layers and MN112 has the fours layers. The Surgical Masks are single use, disposable device, provided non-sterile.

The document you provided is a 510(k) Premarket Notification for surgical masks, which focuses on demonstrating substantial equivalence to a predicate device. It includes non-clinical testing results against specific standards, but it does not describe a study in the context of evaluating an AI/ML-driven device's performance against clinical acceptance criteria. The questions you've asked are typically relevant for AI/ML medical devices.

Since the provided text describes a medical device (surgical masks) that is evaluated based on physical and filtration properties, and not an AI/ML device, many of the requested categories (like number of experts, ground truth, AI assistance effect size, training set) are not applicable.

However, I can extract the acceptance criteria and performance data for the surgical masks from the "Non-Clinical Testing" section.

Here's the information derived from the document, acknowledging that it's for a traditional medical device, not an AI/ML system:

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

2. Sample size used for the test set and the data provenance

• Sample Size:
  • For Fluid Resistance (ASTM F1862M-17), the acceptance criterion mentions "29 out of 32 pass", implying a sample size of 32 items tested for this specific criterion.
  • For other tests (Particulate Filtration, Bacterial Filtration, Differential Pressure, Flammability), the document states "Pass" without specifying the exact number of units tested.
• Data Provenance: The document does not specify the country of origin of the data or whether the tests were retrospective or prospective. These are standard non-clinical tests performed in a lab setting rather than clinical data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This question is not applicable as the device is a surgical mask, and its performance is evaluated against standardized test methods for physical properties (like filtration efficiency and fluid resistance), not against expert interpretations of clinical images or data. Ground truth in this context is established by the methods outlined in standards such as ASTM F1862M-17, ASTM F2299, ASTM F2101-19, EN 14683:2019, and 16 CFR 1610.

4. Adjudication method for the test set

This question is not applicable. The performance is measured directly by scientific instruments and defined protocols within the specified ASTM and EN standards, not through human adjudication or consensus.

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

This question is not applicable as the device is a surgical mask and not an AI-assisted diagnostic or interpretive system. No human reader studies with or without AI assistance were conducted.

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

This question is not applicable as the device is a surgical mask and does not involve any algorithm or AI component performing in a standalone capacity.

7. The type of ground truth used

The "ground truth" for the performance evaluation of these surgical masks is established by physical and material testing standards (e.g., filtration rates, fluid resistance under specified pressures, flammability class) as defined by organizations like ASTM and EN.

8. The sample size for the training set

This question is not applicable. The document describes non-clinical performance testing for a physical product, not the development of an AI/ML model. Therefore, there is no "training set."

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

This question is not applicable for the same reasons as point 8.

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