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510(k) Data Aggregation

    K Number
    K210042
    Date Cleared
    2021-11-17

    (314 days)

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

    The Disposable 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. The Disposable Surgical Masks are single use, disposable device, provided non-sterile.

    Device Description

    Disposable Surgical Mask is composed of three layers and is flat-pleated. The mask materials consist of an outer layer (spun-bond polypropylene), a middle layer, between the outer layer and inner layers (melt-blown polypropylene), and an inner layer (spun-bond polypropylene). Each mask contains ear loops to secure the mask over the users' mouth and face and includes a malleable nose piece (High-density Polyethylene) to provide a firm fit over the nose. There are four models for Disposable Surgical Mask with different colors and sizes. All six models are Ear Loop type in blue or white colors and two sizes including 145mm×90mm and 175×95mm.

    AI/ML Overview

    The provided document is a 510(k) summary for a Disposable Surgical Mask (K210042). It focuses on demonstrating substantial equivalence to a predicate device, rather than proving the device meets acceptance criteria in the context of an AI/ML medical device. The document details performance testing for physical and material properties of the surgical masks.

    Therefore, most of the requested information regarding AI/ML device testing (e.g., sample size for test set, data provenance, number of experts for ground truth, MRMC studies, standalone performance, training set details) is not applicable to this type of device and submission.

    However, I can extract the relevant acceptance criteria and performance data for the Disposable Surgical Mask as presented in the document.

    Here's a breakdown of the available information:

    1. Table of Acceptance Criteria and Reported Device Performance (Non-Clinical/Material Performance):

    This table is directly from the "IX. PERFORMANCE DATA" section of the document. The acceptance criteria and results refer to the physical and barrier performance of the surgical mask material.

    Test MethodologyPurposeAcceptance CriteriaReported Device Performance
    Bacterial Filtration EfficiencyTo determine the bacterial filtration efficiency (BFE)Level 2 ≥ 98%32/32 Passed at ≥99.9%
    Particulate Filtration EfficiencyTo determine the particle filtration efficiency (PFE)Level 2 ≥ 98%32/32 Pass at ≥99.5%
    Differential Pressure (delta-P)To measure breathability/resistance to airflowLevel 2 < 6.0 mmH2O/cm²32/32 Pass at <4.3 mmH2O/cm²
    Resistance to penetration by synthetic blood, minimum pressure in mm Hg for pass resultTo evaluate effectiveness against exposure to blood and other body fluidsLevel 2: No penetration at 120 mmHg32/32 Passed at 120mmHg
    FlammabilityTo measure the flammability of masksLevel 2: Class 132/32 Passed Class 1 requirement
    Cytotoxicity (ISO10993-5)Biological evaluationSubject device extract determined to be non-cytotoxicUnder the conditions of the study, the subject device extract was determined to be non-cytotoxic.
    Irritation (ISO10993-10)Biological evaluationSubject device non-polar and polar extracts determined to be non-irritatingUnder the conditions of the study, the subject device non-polar and polar extracts were determined to be non-irritating.
    Sensitization (ISO10993-10)Biological evaluationSubject device non-polar and polar extracts determined to be non-sensitizingUnder the conditions of the study, the subject device non-polar and polar extracts were determined to be non-sensitizing.

    Study that Proves the Device Meets Acceptance Criteria (as described for this non-AI/ML device):

    The document states: "Non-clinical tests were conducted to verify that the proposed device met all design specifications found in the test methodology and standard using 3 nonconsecutive lots." The "Test Methodology" column in the table above lists the specific ASTM, EN, MIL-M, and ISO standards used for each test. The "Results" column confirms that the device passed all specified acceptance criteria for these physical and biological tests.

    Regarding AI/ML specific questions (which are NOT applicable to this document):

    • 2. Sample sized used for the test set and the data provenance: Not applicable. This is not an AI/ML device. The "sample size" is the number of mask samples tested (e.g., "32/32 Passed"). Data provenance would be from laboratory testing results based on specific mask lots.
    • 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for non-AI/ML medical devices like surgical masks is established via objective physical and biological tests against established industry standards.
    • 4. Adjudication method for the test set: Not applicable.
    • 5. If a multi reader multi case (MRMC) comparative effectiveness study was done: Not applicable.
    • 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
    • 7. The type of ground truth used: For this device, the "ground truth" is defined by compliance with the specified performance standards (e.g., ASTM F2101 for BFE, ASTM F2299 for PFE, 16 CFR Part 1610 for Flammability, ISO 10993-X for biocompatibility).
    • 8. The sample size for the training set: Not applicable. No AI/ML model for training.
    • 9. How the ground truth for the training set was established: Not applicable.

    In summary, this document is a regulatory submission for a traditional medical device (surgical mask) and describes its physical and biological performance testing, not the performance of an AI/ML algorithm.

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