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The Disposable Medical 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 Disposable Medical Masks are single use, three-layer, flat -folded masks with ear loops and nose clamp. The Medical Masks are manufactured with three layers, the inner and outer layers are made of spunbond polypropylene, and the middle layer is made of melt blown polypropylene filter. The ear loops are held in place over the users' mouth and nose by two elastic ear loops welded to the facemask. The elastic ear loops are made of Polyester and spandex. The nose clamp in the layers of facemask is to allow the user to fit the facemask around their nose, which is made of polypropylene with iron wire. The Medical Masks will be provided in blue. The medical masks are provided as non-sterile and are intended to be single use, disposable devices.

This document describes the premarket notification (510(k)) for a Disposable Medical Mask (K202133) by HANGZHOU QIANZHIYA SANITARY PRODUCTS CO., LTD. The objective is to demonstrate substantial equivalence to a predicate device, the Kimberly-Clark KC100 Mask (K110455), based on non-clinical performance and biocompatibility testing. The mask is intended for use in infection control practices to protect both patients and healthcare personnel from microorganisms, body fluids, and particulate material. It is a single-use, disposable, non-sterile device.

Here's an analysis of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size:
  • For Fluid Resistance, Particulate Filtration Efficiency, Bacterial Filtration Efficiency, and Differential Pressure tests: 32 samples per lot, with tests conducted on 3 non-consecutive lots. This means a total of 96 samples (32 x 3) were tested for these performance characteristics.
  • For Biocompatibility tests (Cytotoxicity, Irritation, Sensitization), the sample size is not explicitly stated as a number of devices, but the results refer to "the device" implying representative samples were used.
  • For Flammability, a Class 1 designation was achieved, implying standard textile flammability testing was conducted.
• Data Provenance: The document does not explicitly state the country of origin of the data or whether the studies were retrospective or prospective. Given the submission is from a Chinese manufacturer (HANGZHOU QIANZHIYA SANITARY PRODUCTS CO., LTD) with a Shanghai-based correspondent, it is highly probable the testing was conducted in China or by labs familiar with international standards. These are non-clinical in vitro and ex vivo tests on the device materials and finished product, not human-based studies, so prospective/retrospective distinctions are not directly applicable in the same way they would be for clinical trials.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not applicable to this submission. The "ground truth" for the performance characteristics of a medical mask is established by international and national standards (e.g., ASTM, EN, ISO) that define specific physical and biological properties and the methods to test them. The device's performance is measured against these objective criteria, not against expert human interpretations of images or clinical outcomes.

4. Adjudication Method for the Test Set

This information is not applicable. Adjudication methods (like 2+1, 3+1) are typically used in clinical studies or studies involving human readers where there might be disagreement in interpretations (e.g., diagnosing a condition from an image). Here, the tests involve objective measurements of physical and biological properties against predefined thresholds.

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

This information is not applicable and no such study was conducted. This submission is for a physical medical mask, not an AI-powered diagnostic or assistive device.

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

This information is not applicable as the device is not an algorithm or AI system.

7. The Type of Ground Truth Used

The "ground truth" used for assessing the device's performance is based on established industry standards and guidelines for medical masks. These include:

• ASTM F2100: Standard Specification for Performance of Materials Used In Medical Face Masks (defining Level 1 criteria).
• ASTM F1862: Standard Test Method for Resistance of Medical Face Masks To Penetration by Synthetic Blood.
• ASTM F2101: Standard Test Method for Evaluating the Bacterial Filtration Efficiency (BFE).
• ASTM F2299: Standard Test Method for determining the initial efficiency of materials used in medical face masks to penetration by particulates using latex spheres.
• EN 14683: Medical Face Masks-Requirements and Test Methods (specifically Annex C for Differential Pressure).
• ISO 10993-5: Biological Evaluation of Medical Devices -- Part 5: Tests For In Vitro Cytotoxicity.
• ISO 10993-10: Biological Evaluation of Medical Devices - Part 10: Tests For Irritation And Skin Sensitization.
• 16 CFR 1610: Standard for the Flammability of clothing textiles.

The device's performance is directly measured and compared against the quantitative and qualitative acceptance criteria specified in these standards.

8. The Sample Size for the Training Set

This information is not applicable. This is a physical device, and the testing described is not for a machine learning model that requires a training set.

9. How the Ground Truth for the Training Set Was Established

This information is not applicable. As mentioned above, there is no training set for a machine learning model in this submission.

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