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510(k) Data Aggregation

    K Number
    K201537
    Date Cleared
    2020-09-04

    (88 days)

    Product Code
    Regulation Number
    878.4040
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Protective Face Mask for Medical Use is intended to be worn to protect both the patient and healthcare personnel from transfer of microorganisms, body fluids and particulate material. The face mask is 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.

    Device Description

    The Protective Face Mask for Medical Use is a single use, two-panel, flat-folded mask with ear loops and nose piece. The mask is designed into a C-shape when flat-folded. The C-shaped design allows for an expanded chamber for the mask in use. The mask materials include four layers, the inner and outer layers are made of spun-bond polypropylene, and the two middle layers are melt-blown polypropylene and non-woven polypropylene filters, respectively. The elastic ear loops are made of spandex and polyester, which are welded to the facemask to hold the mask in place over the users' mouth and nose. The elastic ear loops are not made with natural rubber latex. The nose piece is a malleable aluminum strip covered with sponge, which is welded to the facemask top edge to allow the user to fit the facemask around their nose. The mask is a single use, disposable device, provided non-sterile in white color.

    AI/ML Overview

    This document is a 510(k) Premarket Notification submission for a medical device, specifically a "Protective Face Mask for Medical Use." It focuses on demonstrating substantial equivalence to a predicate device based on non-clinical testing.

    Here's an analysis of the acceptance criteria and study proving the device meets them, based on the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance:

    The document provides a "Performance testing" table (Table 2) that directly addresses this point.

    Test ItemsAcceptance CriteriaResults
    Fluid Resistance Performance (ASTM F1862M-17)29 out of 32 pass at 120 mmHg32 out of 32 pass at 120mmHg
    Particulate Filtration Efficiency (ASTM F2299)>98%99.22%
    Bacterial Filtration Efficiency (ASTM F2101-19)>98%99.89%
    Differential Pressure (EN 14683:2019 Annex C)<6.0 mm H2O/cm²4.2 mmH2O/cm²
    Flammability (16 CFR 1610)Class 1 / Non-FlammableClass 1 (not ignited)

    Additionally, a "Biocompatibility testing" table (Table 3) is provided:

    Testing ItemsStandardsResults
    CytotoxicityISO 10993-5:2009Pass (Non-Cytotoxic)
    IrritationISO 10993-10:2010Pass (Non-Irritating)
    SensitizationISO 10993-10:2010Pass (Non-Sensitizing)

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

    • Sample Size for Test Set:
      • For Fluid Resistance Performance, the sample size is explicitly stated as 32 (implied by "32 out of 32 pass").
      • For other performance tests (Particulate Filtration Efficiency, Bacterial Filtration Efficiency, Differential Pressure, Flammability) and all Biocompatibility tests, the exact sample size is not explicitly stated in this document. It is only reported that the tests were done and passed.
    • Data Provenance: The document does not specify the country of origin of the data for the tests. It states the submitter's address is in Jinan, Shandong, China, implying the testing could have been conducted in China, but this is not explicitly confirmed for the test data itself. The testing type is non-clinical testing, which is typically conducted in a laboratory setting, not a retrospective or prospective clinical study on human subjects for this type of device.

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

    This information is not applicable to this submission. The "ground truth" for a surgical mask primarily relies on objective, standardized physical and biological performance tests (e.g., filtration efficiency, fluid resistance) conducted in laboratories, not on human expert consensus or interpretation of images/data, as would be the case for AI/image analysis devices. The standards themselves (e.g., ASTM, ISO, EN) define the accepted methods and criteria.

    4. Adjudication Method for the Test Set:

    This information is not applicable for the same reasons as point 3. Laboratory tests for physical performance of a mask do not involve adjudication by multiple experts in the same way clinical data or imaging studies do.

    5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

    No, an MRMC comparative effectiveness study was not done. The document explicitly states: "No clinical study is included in this submission." This type of study would be relevant for software or AI devices where human performance with and without AI assistance is being evaluated. This is a physical device (surgical mask) whose performance is evaluated through standardized laboratory tests.

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

    Not applicable/No. This is not an algorithm or AI device. The testing conducted was standalone performance of the physical mask itself in laboratory conditions, according to specified standards.

    7. The Type of Ground Truth Used:

    The ground truth used for this device is based on objective, standardized laboratory measurements and adherence to established industrial and medical device standards (e.g., ASTM F1862M-17 for fluid resistance, ASTM F2299 for particulate filtration, ASTM F2101-19 for bacterial filtration, EN 14683:2019 Annex C for differential pressure, 16 CFR 1610 for flammability, and ISO 10993 series for biocompatibility). These standards define the acceptable performance thresholds.

    8. The Sample Size for the Training Set:

    This information is not applicable. This is a physical medical device, not a software/AI device that requires a "training set" for machine learning.

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

    This information is not applicable for the same reason as point 8.

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