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510(k) Data Aggregation

    K Number
    K221402
    Date Cleared
    2023-03-22

    (310 days)

    Product Code
    Regulation Number
    872.3630
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    CEREC Tessera Abutment Block, CEREC Tessera Abutment System

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    CEREC Tessera Abutment System is intended for use in partially or fully edentulous mandibles and maxillae in support of single cement-retained restorations.

    The system comprises three parts:

    • CEREC Tessera Abutment Block
    • TiBase
    • CAD/CAM system.
      The CEREC Tessera ceramic structure cemented to the TiBase is recommended for two-piece hybrid abutments for single tooth restorations and hybrid abutment crowns, used in conjunction with endosseous dental implants.
    Device Description

    The proposed CEREC Tessera Abutment Blocks are intended for fabrication of single cement-retained restorations. The CEREC Tessera Abutment System is comprised of the proposed CEREC Tessera Abutment Block, applicable TiBase, and CAD/CAM systems in both chairside (CEREC chairside software) and labside (inLab labside software) use. The CEREC Tessera ceramic structures are fabricated by milling the proposed CEREC Tessera Abutment Blocks. The CEREC Tessera Abutment Blocks are designed with a pre-drilled screw access channel and anti-rotation feature. The design allows for fabrication of a ceramic structure used to create 2-piece hybrid abutments and hybrid abutment crowns cemented to a TiBase used with dental implant systems. The patient-specific ceramic structure is then cemented to a TiBase to create the two-piece abutment which constitutes the final finished medical device.

    AI/ML Overview

    The provided text describes a 510(k) premarket notification for the "CEREC Tessera Abutment Block, CEREC Tessera Abutment System." This is a dental device, not an AI/ML-driven device. Therefore, many of the requested criteria related to AI/ML studies (such as sample sizes for test and training sets, expert ground truth, adjudication methods, MRMC studies, standalone algorithm performance) are not applicable to this document.

    However, I can extract information regarding acceptance criteria and performance data for the dental device from the provided text.

    Here's the relevant information:

    1. Table of Acceptance Criteria and Reported Device Performance

    CharacteristicAcceptance CriteriaReported Device Performance
    Mechanical StrengthMeets ISO 14801:2016 (Dynamic loading test for endosseous dental implants)Meets ISO 14801
    Flexural Strength≥ 360 MPa (based on predicate device)Average of 705 MPa
    Material PropertiesMeets ISO 6872:2015 Amd 1. 2018 (Dentistry - Ceramic Materials)Meets criteria defined in ISO 6872
    BiocompatibilityMeets ISO 10993 requirements (specifically ISO 10993-1 and ISO 10993-5 for cytotoxicity)Meets ISO 10993 requirements; confirmatory cytotoxicity test performed with satisfactory results
    Cleaning/DisinfectionValidated per ISO 17664-1:2021, ANSI/AAMI ST79:2017, FDA Guidance (March 17, 2015), AAMI TIR-12:2010Manual and automated cleaning and automated disinfection processes validated
    SterilizationAchieves Sterility Assurance Level (SAL) of 10^-6 at 132° C for 4 minutes and 135° C for 3 minutes, meeting ANSI/AAMI ST79:2017, ISO 17665-1, ISO 17665-2Validated using a hybrid method; met specified SAL criteria
    MRI SafetyLabeled as MRI ConditionalThe subject device is labeled MRI Conditional (test data leveraged from K221094)

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

    • Sample Size: Not explicitly stated in terms of number of test samples for each specific test, but the tests were performed "on the proposed CEREC Tessera Abutment System" and "on the proposed CEREC Tessera Abutment Block."
    • Data Provenance: The studies are described as "Non-clinical bench testing" and "Biocompatibility evaluation assessment." The text does not specify the country of origin of the raw data, nor whether it's retrospective or prospective, as these terms are more typically applied to clinical studies.

    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)

    • Not applicable. This is a medical device clearance based on non-clinical bench testing, not an AI/ML diagnostic device requiring expert interpretation for ground truth.

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

    • Not applicable. This is a medical device clearance based on non-clinical bench testing.

    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 medical device clearance based on non-clinical bench testing, not an AI/ML diagnostic device involving human readers.

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

    • Not applicable. This is a medical device clearance based on non-clinical bench testing, not an AI/ML algorithm.

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

    • For the non-clinical tests (mechanical strength, flexural strength, material properties, biocompatibility, cleaning/sterilization processes), the "ground truth" is defined by adherence to established international standards (ISO, ANSI/AAMI), FDA guidance documents, and internal engineering requirements. This is essentially a bench test standard compliance type of ground truth.

    8. The sample size for the training set

    • Not applicable. This is a medical device clearance based on non-clinical bench testing, not an AI/ML device requiring a training set.

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

    • Not applicable. This is a medical device clearance based on non-clinical bench testing, not an AI/ML device requiring a training set.
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