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The surgical face mask is intended to be worn to protect both the patient and healthcare personnel from the transfer of microorganisms, body fluids and particulate material. The face mask 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.

Surgical Face Mask is composed of a mask body, a nose clip, and two ear ropes. The mask body of ear loop type has three layers in structure: the inner and outer layers are made of polypropylene non-woven fabrics, and the middle layer is made of polypropylene melt-blown cloth. The component ratio is 24.5% melt blown cloth and 71.5% PP non-woven. The nose clip 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 PE. The ear ropes are made of nylon and spandex. This is a single use, disposable device(s), provided non-sterile.

The provided text describes the non-clinical performance testing for a medical device, a Surgical Face Mask (K210015). It does not describe a study involving an algorithm, AI assistance, or human readers, which is implied by several of the questions in the prompt. Therefore, I can only address aspects of the prompt that directly relate to the provided text regarding the performance of the surgical face mask.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided document:

Device: Surgical Face Mask (K210015)

Study Type: Non-clinical Performance Testing (physical and biological properties of the mask)

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

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

• Sample Size: For most tests (Fluid Resistance, Particulate Filtration, Bacterial Filtration, Differential Pressure, Flammability), the tests were conducted on "total 3 lots," with results reported as "96/96" or "95 out of 96 samples." For Fluid Resistance, the specific acceptance criteria mention "at least 29 out of 32 specimens," implying a testing batch size of 32 for that particular aspect within each lot. For the biological tests (Cytotoxicity, Irritation, Sensitization), it simply states "All samples met the predetermined acceptance criteria," without specifying the exact number of samples tested, but implies they were sufficient for ISO standard compliance.
• Data Provenance: The document does not explicitly state the country of origin of the data or whether the studies were retrospective or prospective. Given that it's a premarket notification (510(k)) for a new device, the testing would generally be prospective, conducted specifically for this submission. The manufacturer is "Sample King Manufacturing Ltd" located in Shenzhen, China, so it's likely the testing was performed there or by associated labs.

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)

This question is not applicable. The studies described are non-clinical laboratory performance tests (e.g., measuring filtration efficiency, fluid resistance, flammability, and biological compatibility), not studies requiring expert interpretation of medical images or patient outcomes. The "ground truth" is established by the results of standardized laboratory measurements and compliance with specified thresholds, not by expert consensus.

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

This question is not applicable. Adjudication methods like "2+1" or "3+1" are typically used in clinical studies, especially those involving human readers assessing data, to resolve discrepancies or establish a definitive ground truth. The tests performed here are laboratory-based, with quantitative results compared against predefined acceptance criteria.

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. The provided document details the non-clinical performance testing of a physical medical device (surgical face mask), not an AI algorithm or a diagnostic tool that would involve human readers.

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

This question is not applicable. The document describes the performance of a physical surgical face mask, not an algorithm.

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

The "ground truth" for the performance tests of the surgical face mask is defined by the measured values obtained from standardized test methods (e.g., ASTM, ISO, CFR) and their comparison against pre-defined quantitative and qualitative acceptance criteria. For example:

• For Particulate Filtration Efficiency, the ground truth is the measured percentage of particles filtered, compared to the ≥ 98% acceptance criterion.
• For Flammability, the ground truth is whether the material ignited or not, compared to the Class I requirement.
• For Cytotoxicity, the ground truth is the biological response observed in the in-vitro test, compared to the "Non-cytotoxic" acceptance criterion.

8. The sample size for the training set

This question is not applicable. There is no mention of a training set as this study is not for an AI/machine learning device. The "lots" mentioned are production batches from which samples for testing were drawn.

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

This question is not applicable, as there is no training set for a non-AI device.

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