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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

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