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510(k) Data Aggregation

    K Number
    K231552
    Device Name
    Dia-Cem
    Date Cleared
    2023-07-28

    (59 days)

    Product Code
    Regulation Number
    872.3275
    Panel
    Dental
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    Dia-Cem

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use
    • Resin crowns, bridges, inlays and onlays
    • Glass Ceramic, Porcelain crowns, inlays and onlays(includes alumina and zirconia)
    • Metal crowns, bridges, inlays and onlays(includes porcelain-fused-to-metal and composite-to-metal)
    • Metal(prefabricated or cast) and fiber posts
    Device Description

    Dia-Cem is a radiopaque resin cement that can be used in self-cure or light-cure mode. It corresponds to type 2 and 3 of ISO 4049 and contains more than 60% of inorganic filler. Dia-Cem shows high bonding strength on various materials, yet excess material can be easily removed. 3 different shades are available: TR, A2, and A3O.

    AI/ML Overview

    This document describes the premarket notification (510(k)) for the Dia-Cem dental cement. It focuses on demonstrating substantial equivalence to a predicate device, BisCem, rather than presenting a study proving a device meets specific acceptance criteria in the manner of an AI/ML device.

    Therefore, many of the requested categories (2, 3, 4, 5, 6, 7, 8, 9) are not directly applicable as this is a material science characterization for a traditional medical device, not an AI/ML algorithm.

    Here's the information extracted that aligns with your request:

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

    Performance CharacteristicAcceptance Criteria (Predicate)Reported Device Performance (Subject Device - Dia-Cem)
    Working timeA homogeneous and thin film should be formed at 60 sec after the complete of mixing.A homogeneous and thin film should be formed at 60 sec after the complete of mixing.
    Flexural strengthMore than 50 MPaMore than 50 MPa
    Film thicknessNo greater than 50 µmNo greater than 50 µm
    Shear bond strengthEqual or greater than 4 MPaEqual or greater than 4 MPa
    BiocompatibilityBiocompatibleBiocompatible
    Shelf-life2 years2 years

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

    • Not applicable in the provided document. The document describes non-clinical performance data based on ISO standards for material properties, not a clinical study with a test set of patient data.

    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 in the provided document. Ground truth, in the context of expert review, is not relevant for this type of material science testing.

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

    • Not applicable in the provided document.

    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 in the provided document. This is not an AI-assisted device.

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

    • Not applicable in the provided document. This is not an AI/ML algorithm.

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

    • For the non-clinical performance data: The "ground truth" or reference for the device's performance is established by international standards such as ISO 4049:2019, ISO 29022:2013, ISO 7405:2018, ISO 10993-1:2018, ISO 10993-3:2014, ISO 10993-5:2009, ISO 10993-10:2010, and ISO 10993-11:2017. These standards define the methodologies and acceptable limits for the physical, mechanical, and biocompatibility properties of dental cement.

    8. The sample size for the training set

    • Not applicable in the provided document. This device does not use an AI/ML algorithm that requires a training set.

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

    • Not applicable in the provided document.
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