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510(k) Data Aggregation

    K Number
    K211308
    Device Name
    INNI-CERA
    Manufacturer
    Date Cleared
    2021-10-04

    (158 days)

    Product Code
    Regulation Number
    872.6660
    Panel
    Dental
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    INNI-CERA is indicated for use by dental technicians in the construction of custom made all ceramic restorations.

    Coping
    Crown
    Inlay & Onlay
    Veneer
    Bridge(3-unit anterior bridges)

    Device Description

    INNI-CERA is intended for use in fabricating custom-made ceramic restorations using a 3D printer additive manufacturing process. It is a product that is used after fabricating and sintering with a mixture of zirconia powder and binder. The DLP 3D printer used to fabricate the INNICERA is a photocuring lamination method using an STL file (dental restoration shape), which is irradiated with light in the strong UV region (420nm) to cure the photocurable mixture to form a model. This product corresponds to ISO 6872 Type I Class III. The material is used in a 3D printer, which prints the shape determined by an STL file converted from patients' teeth data. 3D printer is not included with the product. INNI-CERA cannot be reused.

    AI/ML Overview

    The provided text describes a 510(k) premarket notification for a dental material, INNI-CERA. The submission focuses on demonstrating substantial equivalence to a predicate device, not on proving device performance against acceptance criteria in a typical clinical study with human readers or AI.

    Therefore, many of the requested categories for acceptance criteria and study details are not applicable in this context. The document primarily describes bench testing to ensure the material meets established standards for dental ceramics.

    Here's a breakdown of the applicable information:

    1. Table of Acceptance Criteria and Reported Device Performance

    Performance MetricAcceptance Criteria (ISO 6872 requirements)Reported Device Performance (INNI-CERA)
    Flexural Strength>300 MPa>300 MPa
    Chemical Solubility< 100 µg/cm²< 100 µg/cm²
    RadioactivityActivity concentration of uranium238 < 1.0 Bq g⁻¹Activity concentration of uranium238 < 1.0 Bq g⁻¹
    BiocompatibilityMeets EN ISO 10993-1 requirementsMet requirements (verified by various tests listed)
    Shelf LifeMeets ISO 6872:2015 after accelerated aging3 months (confirmed by accelerated aging test)

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

    This information is not provided in the document. The tests are bench tests on the material itself, not on patient data or a test set in the traditional sense of diagnostic AI.

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

    Not applicable. Ground truth for material properties is established through standardized laboratory testing protocols (e.g., ISO 6872), not by expert consensus on clinical cases.

    4. Adjudication Method for the Test Set

    Not applicable. See explanation for point 3.

    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

    Not applicable. This is a material for dental restorations, not a diagnostic AI device requiring human reader studies.

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

    Not applicable. This is a material for dental restorations, not an algorithm.

    7. The Type of Ground Truth Used

    The ground truth used for evaluating INNI-CERA's performance is based on established international standards for dental ceramic materials (ISO 6872:2015) and biocompatibility standards (ISO 10993-1). This involves objective measurements of material properties in a laboratory setting.

    8. The Sample Size for the Training Set

    Not applicable. INNI-CERA is a material, not a machine learning model, so there is no training set in the context of AI.

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

    Not applicable. See explanation for point 8.

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