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The Expanded Chamber Surgical Mask is 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 HALYARD* FLUIDSHIELD* 2 Surgical Mask with Expanded Chamber that are the subject of this submission are provided with a visor (39124). These masks are single use, disposable devices, provided non-sterile.

The subject devices, the HALYARD* FLUIDSHIELD* 2 Surgical Mask with Expanded Chamber with SO SOFT* Lining (39123) and HALYARD* FLUIDSHIELD* 2 Surgical Mask with Expanded Chamber with SO SOFT* Lining and Visor (39124), are the same devices as cleared under K143287.

The subject devices are ASTM F2100 Level 2 (blue and white in color) Surgical Masks. The HALYARD* FLUIDSHIELD* 2 Surgical Mask with Expanded Chamber with SO SOFT* Lining (39123) and HALYARD* FLUIDSHIELD* 2 Surgical Mask with Expanded Chamber with SO SOFT* Lining and Visor (39124) are a four- layer mask, constructed of well-known non-woven materials used in facial protection. A malleable nosepiece is placed within the bindings for comfort and individualized fit around the wearer's nose. When compared to a pleated/flat mask, the subject devices have a "duckbill" shape, which expands the volume of the chamber created when the mask is worn.

The provided text is a 510(k) Summary for a medical device (surgical masks). This type of document focuses on demonstrating substantial equivalence to a legally marketed predicate device through non-clinical performance testing. It does not typically contain information about AI/ML models, human reader studies, or detailed ground truth methodologies used in the context of advanced diagnostic or imaging AI.

Therefore, many of the requested points related to AI/ML device studies are not applicable to this document.

Here's the information that can be extracted and what is not applicable:

1. 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):
The document does not specify exact sample sizes for each non-clinical test (e.g., number of masks tested for BFE, PFE, etc.). It describes non-clinical performance testing against established standards (ASTM, EN, ISO). Data provenance (country of origin, retrospective/prospective) is not mentioned as these are laboratory-based material performance tests.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Not applicable. These are non-clinical material performance tests, not studies requiring expert interpretation of medical data (like radiology images). The ground truth is defined by the technical specifications and outcomes of the standardized tests.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
Not applicable. This is not an AI/ML or diagnostic study requiring expert adjudication of interpretations.

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:
Not applicable. This is for a surgical mask, not an AI-assisted diagnostic device. No "human readers" or "AI assistance" are involved in its performance evaluation.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
Not applicable. This is for a surgical mask, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
The "ground truth" for the non-clinical tests is based on the specific methodologies and pass/fail criteria defined by the international and national standards cited (e.g., ASTM F2101 for Bacterial Filtration Efficiency, ISO 10993 for biocompatibility). The tests objectively measure physical and biological properties against quantifiable benchmarks.

8. The sample size for the training set:
Not applicable. This device is not an AI/ML product developed with a training set.

9. How the ground truth for the training set was established:
Not applicable. This device is not an AI/ML product.

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