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The Spin Care Disposable Protective Mask Level 3 is intended to be worn to protect both the patient and healthcare personnel from transfer of microorganisms, body fluids and particulate material. These protective 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 Spin Care Disposable Protective Mask Level 3 is a single use three-layer level 3 surgical mask. It houses a melt blown polypropylene filter between an outer and inner layer of spunbond polypropylene that covers the nose and mouth of the end user and held in place by a pliable nose piece and ear loops. The mask is provided white, non-sterile and is flat-pleated. Contact duration is less than 24 hours. This device is not made with natural rubber latex.

The provided text is for a 510(k) premarket notification for a disposable protective mask, not an AI/ML medical device. Therefore, it does not contain the information requested in the prompt regarding acceptance criteria and study details for an AI/ML device.

Specifically, the document focuses on demonstrating substantial equivalence of the mask to a predicate device through:

• Indications for Use: Matching the intended use of the mask.
• Technological Characteristics: Comparing materials, dimensions, and style.
• Performance Testing: Showing the mask meets established ASTM and ISO standards for fluid resistance, particle filtration efficiency, bacterial filtration efficiency, flammability, differential pressure, and biocompatibility (cytotoxicity, irritation, sensitization).

There is no mention of a software device, AI/ML algorithm, or any associated studies that would involve:

• A table of acceptance criteria for AI performance metrics (e.g., sensitivity, specificity, AUC).
• Test set sample sizes with data provenance.
• Number and qualifications of experts for ground truth establishment.
• Adjudication methods.
• MRMC studies or effect sizes for human reader improvement.
• Standalone algorithm performance.
• Ground truth types like expert consensus or pathology for AI.
• Training set sample sizes or ground truth establishment for training data.
• Any kind of "study" that involves an algorithm's performance on a dataset of patient images or data.

Therefore, I cannot extract the requested information from the provided text. The document describes the testing for a physical medical device (a surgical mask), not a digital or AI-based one.

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