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510(k) Data Aggregation

    K Number
    K972946
    Device Name
    AURELUX 77
    Date Cleared
    1997-12-19

    (182 days)

    Product Code
    Regulation Number
    872.3060
    Panel
    Dental
    Reference & Predicate Devices
    N/A
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    Dental casting alloy used in combination with dental ceramics for fabrication of metallo-ceramic restoration.

    Device Description

    High gold casting alloy

    AI/ML Overview

    The provided document is a 510(k) summary for a dental casting alloy, AURELUX 77. This document focuses on demonstrating substantial equivalence to a predicate device (Jelenko Jel 96) rather than proving device performance against specific acceptance criteria in a clinical or standalone study as one would expect for an AI/CADe device.

    Therefore, the information required to answer the prompt (acceptance criteria, study details, sample sizes, ground truth establishment, expert qualifications, adjudication methods, MRMC studies, or standalone algorithm performance) is not present in this document because it describes the submission of a material device, not a software or AI-driven device.

    However, I can extract the comparison data between the new device and the predicate device, which serves as the "performance" demonstrated for this type of submission.

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

    The acceptance criteria for this type of device are intrinsic to demonstrating "substantial equivalence" to a legally marketed predicate device. This means the new device's characteristics should be similar enough to the predicate device that it doesn't raise new questions of safety or effectiveness. The comparison table below effectively acts as the "performance" data against these implicit acceptance criteria of substantial equivalence.

    CharacteristicAcceptance Criteria (Predicate: Jel 96)Reported Device Performance (Aurelux 77)
    Composition (weight%)
    Au7876.6
    Ag1.21.2
    Pt69.9
    Pd129.3
    Cu4.50.5
    Fe10.2
    Snx (<1%)0.6
    Inx (<1%)1.5
    Irx (<1%)0
    Noble metal content> 95% (implied by individual components)> 95% (as stated in discussion)
    Base metal concentrationImplied by individual components< 1% difference (as stated in discussion)
    Physical/Mechanical
    Melting point range (solid.)1170 °C1150 °C
    Melting point range (liquid)1260 °C1230 °C
    Hardness (Vickers 5/30), softN/A200
    Hardness (Vickers 5/30), hard255245
    Yield strength (MPa), softN/A350
    Yield strength (MPa), hard655650
    Elongation (%), softN/A19
    Elongation (%), hard411
    CTE (x10-6/°C)14.514.1
    Density (g/cm3)17.218.5

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

    This information is not applicable and not provided in the document. The "study" here is a comparison of material properties, not a clinical trial with a test set of patients or images. The "data" are the results of material tests (chemical composition analysis, melting point tests, hardness tests, etc.), which are inherent properties of the alloy. The provenance would be the laboratory where these tests were conducted, which is not specified but would typically be an internal or contracted materials testing lab.

    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 is not applicable. "Ground truth" in the context of an AI/CADe device refers to highly accurate clinical diagnoses or annotations. For a material science submission, "ground truth" is established through standardized laboratory testing yielding objective physical and chemical measurements. There are no human experts establishing ground truth in the clinical sense for this type of submission.

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

    This is not applicable. Adjudication methods are relevant for human interpretation tasks, especially in AI/CADe studies where there might be disagreement among experts. Here, the "test set" consists of laboratory measurements, not human interpretations.

    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

    This is not applicable. An MRMC study is relevant for evaluating the impact of AI on human reader performance in diagnostic tasks. This document describes a dental casting alloy, not an AI or diagnostic software.

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

    This is not applicable. Standalone performance refers to the performance of an AI algorithm independent of human intervention. This document describes a physical material.

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

    For this material device, the "ground truth" (or the reference standard for comparison) is derived from standardized physical and chemical laboratory testing and measurements performed on both the predicate device (Jel 96) and the new device (Aurelux 77) according to recognized test methods (ANSI/ADA 5 and ISO 9693). There is no "pathology" or "outcomes data" in the clinical sense for this type of submission.

    8. The sample size for the training set

    This is not applicable. "Training set" refers to data used to train an AI algorithm. This document describes a physical material.

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

    This is not applicable. There is no training set or AI algorithm in this submission.

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