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Winner Medical facemask / shield are intended to be used by operating room personnel during surgical procedures to protect both the surgical patient and the operating room personnel from transfer of microorganisms, body fluids and particulate matter.

These masks are pleated 3-ply masks with an inner and outer layer that sandwich a polypropylene filter material. On some masks a plastic shield with a non-fog coating is incorporated. The masks are identical to the predicate devices in design, materials used and performance in non-clinical testing. This testing included bacterial filtration efficiency (BFE), pressure differential (Delta P), flammability, ISO skin irritation and sensitization.

The provided document is a 510(k) summary for surgical masks. It focuses on demonstrating substantial equivalence to predicate devices based on non-clinical testing. This type of regulatory submission in the medical device field typically does not involve the types of acceptance criteria and studies (e.g., MRMC studies, expert consensus for ground truth on a large dataset, standalone algorithm performance) that you are asking about, which are more common for AI/ML-based diagnostic or clinical decision support devices.

Therefore, much of the requested information cannot be extracted from this document, as it describes a different kind of regulatory submission and testing. However, I can extract what is relevant to the information provided.

Here's a breakdown based on the provided text:

Acceptance Criteria and Device Performance (Non-Clinical)

The device (Winner Medical Facemasks) was compared to predicate devices through non-clinical testing. The document states that the masks are "identical to the predicate devices in design, materials used and performance in non-clinical testing." This implies that the acceptance criteria were met if the performance in these non-clinical tests was equivalent to the predicate devices.

Study Details (Based on available information)

1. Sample size used for the test set and the data provenance:

   • Sample Size: Not specified in terms of number of masks tested or number of trials for each non-clinical test.
   • Data Provenance: The testing was "non-clinical testing." The document does not specify the country of origin of the data related to these specific tests, but the submitter is based in China, and the U.S. Agent is in California, USA. It's retrospective in the sense that the data was collected and then submitted.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. The "ground truth" for non-clinical performance tests like BFE or flammability is determined by standardized laboratory measurements against established test methods, not by expert consensus in a clinical diagnostic sense.

3. Adjudication method for the test set: Not applicable. Laboratory tests have defined protocols, and the results are typically quantitative measurements, not subjective adjudications.

4. 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. This is a surgical mask, not an AI-powered diagnostic device. No human reader studies (with or without AI assistance) are relevant or mentioned.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable. This is a physical medical device (surgical mask), not an algorithm or AI system.

6. The type of ground truth used: For the non-clinical tests, the "ground truth" is established by the specified standards and protocols for each test (e.g., standardized methods for measuring BFE, Delta P, flammability, skin irritation, and sensitization). The performance of the predicate device serves as the benchmark for substantial equivalence.

7. The sample size for the training set: Not applicable. There is no AI/ML model or "training set" in the context of this device and its regulatory submission.

8. How the ground truth for the training set was established: Not applicable. No training set exists for this type of device.

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