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510(k) Data Aggregation

    K Number
    K250826
    Device Name
    Multi M Series
    Manufacturer
    Date Cleared
    2025-06-12

    (86 days)

    Product Code
    Regulation Number
    872.6660
    Panel
    Dental
    Reference & Predicate Devices
    Predicate For
    N/A
    Why did this record match?
    Reference Devices :

    K161219

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

    The Multi M Series is used in the manufacture of dental prosthesis.

    Device Description

    Multi M Series is zirconia-based ceramic provided in various shapes such as round and it is used to manufacture cores of all ceramic crowns and is classified into ISO 6872 Type 2 Class 5. It is used to manufacture ceramic restorations through cutting process by dental MAD/MAM, computer-assisted design system, or CAD/CAM system. The subject device offers 278 different shades to meet the needs of different patients' tooth colors. It also offers various shape types to be used with various jigs for CAD/CAM milling machine. Multi M Series offers various types as follows: W-type, Z-type. These different types are to accommodate the different types of the jig that is built-in CAD/CAM machine. Users can choose a type which fits the jig they have.

    AI/ML Overview

    This document is a 510(k) clearance letter for a medical device called "Multi M Series", which is a dental material. The standard 510(k) clearance process by the FDA does not typically include the kind of clinical study details usually associated with AI/ML-based diagnostic devices (e.g., sample size for test sets, expert consensus, MRMC studies, effect size of AI assistance). This device is a material, not a diagnostic algorithm.

    Therefore, many of the requested points regarding acceptance criteria and study details (like sample size for test set, number of experts, adjudication methods, MRMC studies, standalone performance) are not applicable to this type of device and submission. The "study" here refers to non-clinical performance and biocompatibility testing against international standards.

    However, I can extract the relevant information where it exists:


    The device in question, "Multi M Series," is a dental material (zirconia-based ceramic) used to manufacture dental prosthetics, not an AI-powered diagnostic device. Therefore, the "acceptance criteria" and "study" described are based on non-clinical performance and biocompatibility testing against established international standards for dental materials, rather than clinical efficacy studies with human subjects or AI algorithm performance benchmarks.

    Acceptance Criteria and Device Performance

    The acceptance criteria are derived from ISO 6872 (for ceramic materials) and ISO 10993 (for biocompatibility).

    1. Table of Acceptance Criteria and Reported Device Performance

    Criterion TypeStandard/RequirementPredicate Device (Zmaxx T Series) PerformanceSubject Device (Multi M Series) PerformanceMeets Acceptance Criteria?
    Mechanical/Physical
    CTEISO 6872(10.6 ± 0.5) × 10⁻⁶ K⁻¹(10.6 ± 0.5) × 10⁻⁶ K⁻¹Yes
    Flexural StrengthISO 6872> 600 MPa> 800 MPaYes (exceeds predicate)
    Chemical SolubilityISO 6872< 2000 µg/cm²< 100 µg/cm²Yes (exceeds predicate)
    Biocompatibility
    CytotoxicityISO 10993-5Non-toxic and biocompatibleNon-toxic and biocompatibleYes
    SensitizationISO 10993-10Non-toxic and biocompatibleNon-toxic and biocompatibleYes
    IrritationISO 10993-10Non-toxic and biocompatibleNon-toxic and biocompatibleYes
    Systemic ToxicityISO 10993-11Non-toxic and biocompatibleNon-toxic and biocompatibleYes

    Note: The reported performance for "Multi M Series" either meets or exceeds the requirements set by the ISO standards and, in some cases, the performance of the predicate device (e.g., higher flexural strength, lower chemical solubility).

    2. Sample size used for the test set and the data provenance

    The document does not specify exact sample sizes for each mechanical or chemical test. Typically, these are defined within the referenced ISO standards (e.g., ISO 6872 specifies sample numbers for flexural strength testing).
    The testing was conducted by DMAX Co., Ltd., which is based in Daegu, Korea. The data would be considered prospective, as it was generated specifically for this 510(k) submission.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

    This is not applicable as the device is a material, not a diagnostic or AI device requiring expert interpretation for ground truth. "Ground truth" for this device is based on objective, quantifiable physical, chemical, and biological measurements performed according to standardized protocols.

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

    This is not applicable for the same reasons as above. There is no expert adjudication for material property 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

    This is not applicable. This is not an AI-assisted diagnostic device, and therefore, no MRMC study or AI assistance evaluation was performed.

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

    This is not applicable as the device is a dental material, not an algorithm.

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

    The "ground truth" for this device relies on objective measurements defined by international standards:

    • Physical/Chemical Properties: Measured values obtained through standardized laboratory tests (e.g., flexural strength, chemical solubility, CTE) as per ISO 6872 and ISO 13356. These are quantitative and inherently objective.
    • Biocompatibility: Results from established in vitro and in vivo toxicology tests (e.g., cytotoxicity, sensitization, irritation, systemic toxicity) as per ISO 10993 series, which assess the material's interaction with biological systems based on predetermined endpoints.

    8. The sample size for the training set

    This is not applicable as this is a physical material, not a software algorithm requiring a training set.

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

    This is not applicable as this is a physical material, not a software algorithm.

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