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510(k) Data Aggregation

    K Number
    K210115
    Date Cleared
    2021-07-29

    (191 days)

    Product Code
    Regulation Number
    872.3750
    Panel
    Dental
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    FiteBac® CC OrthoSeal with K18 QAMS is a light cure primer intended to prepare the tooth surface prior to bonding orthodontic appliances to etched enamel.

    Device Description

    The FiteBac® CC OrthoSeal is a 5% K18 Quaternary Ammonium Methacrylate (QAMS) functionalized, light cured orthodontic sealant used to prepare etched enamel for orthodontic bonding.

    AI/ML Overview

    The provided text describes the 510(k) summary for the FiteBac® CC OrthoSeal device. Here's a breakdown of the requested information:

    1. A table of acceptance criteria and the reported device performance

    The document doesn't explicitly state "acceptance criteria" in a quantitative, pass/fail format for the device's performance characteristics. Instead, it categorizes performance testing, and the reported performance is described as being "acceptable" and not raising "any new issues of safety or effectiveness" in comparison to predicate devices. The primary performance characteristic mentioned is "Bond Strength Testing."

    Acceptance Criteria (Implied)Reported Device Performance
    Bond Strength (Equivalent to predicate devices)Results of all conducted testing including Bond Strength Testing was found acceptable and did not raise any new issues of safety or effectiveness. (Implied equivalence to predicate devices)
    Biocompatibility (Meeting ISO standards)Sensitization: Acceptable Cytotoxicity: Acceptable Irritation: Acceptable (Complied with ISO 10993-1, ISO 10993-5, ISO 10993-10)
    Cure Time10 seconds
    Wavelength< 600 mW/cm²

    2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

    The document does not explicitly state the sample size used for the test set for "Bond Strength Testing" or the data provenance. It only mentions that "non-clinical testing" was performed.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

    This information is not provided in the document. The testing described is non-clinical bench testing, not an effectiveness study requiring expert adjudication of patient data.

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

    This information is not applicable as the testing described is non-clinical bench testing, not an effectiveness study involving human interpretation.

    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

    A multi-reader multi-case (MRMC) comparative effectiveness study was not done. This device is a light cure primer for orthodontic bonding, not an AI-assisted diagnostic tool.

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

    This question is not applicable as the device is a chemical primer, not an algorithm. The non-clinical bench testing represents a "standalone" evaluation of the device's physical and chemical properties.

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

    For Bond Strength Testing, the ground truth would be based on established engineering and materials science principles and validated testing methods for measuring adhesive bond strength. For Biocompatibility, the ground truth is established by the specified ISO standards (ISO 10993 series), which define acceptable biological responses.

    8. The sample size for the training set

    This is not applicable. The device is a chemical primer, not a machine learning model that requires a training set. The "testing summary" refers to non-clinical bench testing, not data used to train an algorithm.

    9. How the ground truth for the training set was established

    This is not applicable since there is no training set for this device.

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