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510(k) Data Aggregation

    K Number
    K202650
    Date Cleared
    2021-03-24

    (191 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 device is 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.

    Device Description

    The Medical disposable face mask non sterile is single use, three-layer, flat-folded masks with ear loops and nose piece. The Medical disposable face mask non sterile is 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 outer non-woven cloth is blue and is dyed by colorant of Reactive Blue 2, and the inner non-woven cloth is white and is not dyed by colorant. 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 not made with natural rubber latex. The nose piece in the layers of facemask is to allow the user to fit the facemask around their nose, which is made of malleable polyethylene wire. The Medical disposable face mask non sterile will be provided in blue. The Medical disposable face mask non sterile is sold non-sterile and are intended to be single use, disposable devices.

    AI/ML Overview

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

    1. Table of Acceptance Criteria and Reported Device Performance

    ItemAcceptance CriteriaReported Device Performance
    Fluid Resistance ASTM F186229 out of 32 pass at 120mmHg3 batches, synthetic blood penetration of 32 test articles per each batch is none seen at 120mmHg test pressure. (This means 32 out of 32 passed for each of the 3 batches, exceeding the 29 out of 32 acceptance criteria)
    Particulate Filtration Efficiency ASTM F2299Level 2 ≥ 98%3 batches, average PFE of 32 test articles per each batch: >99.986%, >99.986%, >99.982%; standard deviation: 0.0123, 0.0142, 0.0389. (All results are significantly higher than the 98% acceptance criteria.)
    Bacterial Filtration Efficiency ASTM F2101Level 2 ≥ 98%3 batches, BFE of 32 test articles per each batch: >99.9%, >99.9%, >99.9%. (All results are significantly higher than the 98% acceptance criteria.)
    Differential Pressure (Delta P) EN 14683:2019+AC:2019 Annex C, Flow rate 8 L/minLevel 2 < 6.0mmH2O/cm²3 batches, Delta P of 32 test articles per each batch: 2.7-3.2 mmH2O/cm², 2.7-2.9 mmH2O/cm², 2.6-3.0mmH2O/cm². (All results are well below the 6.0mmH2O/cm² acceptance criteria.)
    Flammability 16 CFR 1610Class 1Class 1 (3 batches, 32 test articles per each batch are ignited, but extinguished). (Meets the Class 1 requirement.)
    CytotoxicityThe device is non-cytotoxicUnder the conditions of the study, the device is non-cytotoxic. (Pass)
    IrritationThe device is non-irritatingUnder the conditions of the study, the device is non-irritating. (Pass)
    SensitizationThe device is non-sensitizingUnder the conditions of the study, the device is non-sensitizing. (Pass)

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

    The exact location of the testing is not explicitly stated, but the submission is from a company in China. The nature of these tests (e.g., fluid resistance, filtration efficiency) suggests they are laboratory-based performance tests, not clinical studies involving human subjects. Therefore, the data provenance is likely from a certified testing laboratory, and the data is prospective in the sense that the tests were conducted specifically for this submission on newly manufactured devices.

    • Fluid Resistance, Particulate Filtration Efficiency, Bacterial Filtration Efficiency, Differential Pressure, Flammability: For each of these tests, 3 batches were tested, with 32 test articles per each batch. This means a total of 96 samples were tested for each performance characteristic.
    • Biocompatibility (Cytotoxicity, Irritation, Sensitization): The document states "Under the conditions of the study, the device is non-cytotoxic/non-irritating/non-sensitizing," but does not explicitly state the number of samples or animals used for these specific tests within this summary. However, these tests are typically conducted according to ISO 10993 standards, which outline sample size requirements.

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

    This document describes non-clinical performance and biocompatibility testing of a medical disposable face mask. It does not involve "ground truth" derived from expert interpretation of medical images or clinical outcomes in the usual sense for AI/medical device submissions.

    These tests are evaluated against established international standards and regulatory guidance (e.g., ASTM F1862, ASTM F2299, ASTM F2101, EN 14683, 16 CFR 1610, ISO 10993). The "ground truth" here is the pass/fail criteria defined by these standards, which are based on scientific principles and consensus within the relevant industries. The results are objective measurements rather than subjective expert judgments.

    Therefore, the concept of "number of experts used to establish the ground truth" and their qualifications is not applicable in this context. The "experts" involved would be the qualified personnel performing the laboratory tests according to the defined protocols.

    4. Adjudication Method for the Test Set

    As this is objective non-clinical testing against defined standards, an adjudication method like 2+1, 3+1, or none, typically used for resolving discrepancies in expert interpretations (e.g., of medical images), is not applicable. The results are quantitative measurements interpreted against established numerical or qualitative criteria (e.g., "none seen," ">98%", "<6.0mmH2O/cm²"). Any discrepancies would typically be resolved through re-testing or review of the testing methodology by the testing laboratory's quality control system.

    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

    No, an MRMC comparative effectiveness study was not done. This submission is for a medical disposable face mask, which is a physical device, not an AI-powered diagnostic or assistive tool for human readers. Therefore, this question is not applicable.

    6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

    No, a standalone algorithm performance study was not done. As explained above, this device is a physical medical disposable face mask, not an algorithm. Therefore, this question is not applicable.

    7. The Type of Ground Truth Used

    The ground truth used for these performance and biocompatibility tests is based on established industry standards and regulatory requirements. These standards define the acceptable performance metrics and test methodologies. For example:

    • Fluid Resistance: Defined by ASTM F1862.
    • Filtration Efficiency: Defined by ASTM F2299 and ASTM F2101.
    • Differential Pressure: Defined by EN 14683.
    • Flammability: Defined by 16 CFR 1610.
    • Biocompatibility: Defined by ISO 10993-1, ISO 10993-5 (Cytotoxicity), ISO 10993-10 (Irritation and Sensitization).

    These standards provide objective criteria for evaluating the device's technical performance and safety.

    8. The Sample Size for the Training Set

    Not Applicable. This submission is for a physical medical device (face mask), not an AI algorithm. Therefore, there is no "training set."

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

    Not Applicable. As there is no AI algorithm, there is no training set and no ground truth for a training set.

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