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The Surgical Face Masks are intended to be worn to protect both the patient and healthcare personnel from transfer of microorganisms, body fluids and particulate material. These 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 proposed devices are three-layer, flat pleated masks. A Surgical Face Mask is composed of a mask body, a nose piece and two ear loops. The mask body is manufactured with three layers, the inner layer and the outer layer are made of spunbond polypropylene nonwoven fabric, and the middle layer is made of meltblown polypropylene nonwoven fabric. The model of proposed device, ear-loop, is held in place over the user's mouth and nose by two elastic ear loops welded to the mask body. The elastic ear loops are knitted elastic loops (made of polyester and spandex). The nose piece is in the layers of face mask to allow the user to fit the face mask around their nose, which is a steel wire with polyethylene covering. The proposed devices are provided non-sterile and are intended to be single use, disposable devices.

The provided document is a 510(k) Premarket Notification for a Surgical Face Mask. This type of submission focuses on demonstrating substantial equivalence to a predicate device, primarily through non-clinical performance testing, rather than a clinical study establishing efficacy with defined acceptance criteria for AI/ML performance.

Therefore, the information requested in the prompt regarding AI/ML device acceptance criteria, study design (test set, ground truth, experts, MRMC studies), and training set details cannot be found in this document. This document is for a physical medical device (surgical face mask) and not an AI/ML powered device.

However, I can provide the acceptance criteria and study information for the physical properties and biocompatibility of the surgical face mask, which are the main performance aspects evaluated in this 510(k) submission.

Here's the information based on the document provided, re-interpreting "acceptance criteria" and "study" in the context of a physical device:

Acceptance Criteria and Study for the Surgical Face Mask (Physical Device)

The device being evaluated is a Surgical Face Mask, for which the performance is assessed based on physical properties and biocompatibility, specified by recognized standards.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Sample Size: The document does not explicitly state the numerical sample size for each performance test (e.g., how many masks were tested for BFE). It generally states that "the following performance tests have been conducted to demonstrate the effectiveness of device."
• Data Provenance: The tests were conducted on the "proposed device" (Guangdong Winsun Personal Care Products Co.,Ltd's Surgical Face Mask). The location where the testing was performed is not specified, but the applicant and contact persons are based in China. The data is retrospective in the sense that it was collected prior to the 510(k) submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• This is not applicable as the "ground truth" for a physical device like a surgical mask is established by validated laboratory test methods performed according to international standards (e.g., ASTM, EN, ISO, CFR). It does not involve human expert consensus or adjudication in the way an AI/ML diagnostic device's ground truth would. The "experts" are the qualified laboratory personnel performing the standardized tests.

4. Adjudication method for the test set

• Not applicable. Performance is determined directly by objective measurements from standardized tests, not by human 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 document is for a physical medical device (surgical face mask), not an AI/ML product. Therefore, no MRMC study, human readers, or AI assistance is relevant.

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

• Not applicable. This document describes a physical medical device, not an algorithm.

7. The type of ground truth used

• The "ground truth" for this device's performance is defined by established international and national standards for physical and chemical properties (e.g., ASTM F2100-19, ASTM F1862, ASTM F2299, ASTM F2101, EN 14683, 16 CFR Part 1610, ISO 10993-5/10). Compliance with these standards through laboratory testing serves as the basis for demonstrating performance and safety.

8. The sample size for the training set

• Not applicable. This is a physical device, and there is no "training set" in the context of machine learning.

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

• Not applicable. As there is no training set for a physical device, no ground truth was established in this context.

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