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The Disposable Medical Surgical Masks is intended to be worn to protect both the patient and healthcare personnel from the transfer of microorganisms, body fluids, and particulate material. The Single-Use Surgical Mask with Ear Loop 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 Disposable Medical Surgical Masks is intended to be worn to protect both the patient and healthcare personnel from the transfer of microorganisms, body fluids, and particulate material. The Disposable Medical Surgical Masks 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 device(s) are Blue color, and Flat Pleated type mask, utilizing Ear Loops' way for wearing, and they all have Nose Piece design for fitting the facemask around the nose.

The proposed device(s) are manufactured with three layers, the inner and outer layers are made of spun-bond non-woven, and the middle layer is made of Melt-blown non-woven fabric.

The Disposable Medical Surgical Masks is held in place over the user's mouth and nose by two elastic ear loops welded to the facemask. The elastic ear loops are not made with Polyurethane fiber and Nylon.

The nose piece contained in the proposed device(s) is in the layers of the facemask to allow the user to fit the facemask around their nose, which is made of PE and iron wire.

The proposed device(s) are sold non-sterile and are intended to be single-use, disposable devices.

The proposed device(s) are meet the Level 3 Barrier requirements per ASTM F2100-19.

This document is a 510(k) Premarket Notification for a medical device (Disposable Medical Surgical Masks). It is a regulatory submission to the FDA demonstrating that a new device is substantially equivalent to a legally marketed predicate device. As such, it focuses on comparing the new device to the predicate device based on non-clinical performance and specifications.

Therefore, many of the typical acceptance criteria and study details relevant to AI/ML-based medical devices (like MRMC studies, expert adjudication, separate training/test sets for AI, human-in-the-loop performance, etc.) are not applicable to this document, as it describes a physical medical device (surgical masks) and its performance based on standardized material testing.

Here's an analysis based on the provided document:

Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device are based on meeting established industry standards for surgical masks, primarily ASTM F2100-19 for medical face mask materials, and other related standards (ISO 10993 for biocompatibility, ASTM F1862 for fluid resistance, etc.). The reported device performance is presented in comparison to these standards and a predicate device.

Table of Acceptance Criteria and Reported Device Performance (Summary from Section 6 and 7):

Study Details (for a physical product, not an AI/ML algorithm)

It's important to reiterate that this is a submission for a physical medical device (surgical mask), not an AI/ML algorithm. Therefore, many standard AI/ML study components do not apply.

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

   • Test Set: No explicit "test set" in the AI/ML sense is mentioned. Instead, samples of the manufactured masks were subjected to various standardized non-clinical tests. The document does not specify the number of masks or samples tested for each criterion (e.g., how many masks were tested for BFE, how many for fluid resistance).
   • Data Provenance: The tests were conducted by the manufacturer/sponsor (Tianjin Teda Jinshan Easy Packing Manufacture Co., Ltd., China) as part of their verification process for the 510(k) submission. This would be considered "prospective" in the sense that the tests were performed specifically for this submission, on samples of the device.

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

   • Not Applicable. Ground truth in the context of a physical device like a surgical mask is established by validated laboratory testing methods and compliance with published standards (e.g., ASTM, ISO). There are no human "experts" establishing a subjective "ground truth" for each mask. The "ground truth" is the quantitative result from the standardized test.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • None. Adjudication methods are relevant for subjective interpretations, typically in image analysis or clinical assessments. For standardized physical material testing, the results are quantitative and objective measurements from the validated test methods.

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 physical device, not an AI-assisted diagnostic tool.

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

   • Not Applicable. This is a physical device, not an AI algorithm. Its performance is inherent to its materials and construction.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • Objective Laboratory Test Results against Established Standards: The "ground truth" for the performance of the Disposable Medical Surgical Masks is the objective measurement obtained through specific, validated laboratory test methods (e.g., ASTM F2101 for BFE, ASTM F1862 for fluid resistance) and comparison against the specified acceptance criteria within those standards (e.g., ASTM F2100 Level 3 requirements).

7. The sample size for the training set:

   • Not Applicable. This is a manufactured physical device, not an AI/ML model that requires a training set. The manufacturing process is analogous to a "training" phase where the design and materials are refined to meet specifications.

8. How the ground truth for the training set was established:

   • Not Applicable. As above, no training set for an AI algorithm. The manufacturing process of a physical device involves quality control and adherence to specifications to ensure the final product meets its intended performance, which is then verified through non-clinical testing.

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