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510(k) Data Aggregation

    K Number
    K150571
    Device Name
    3i T3 Short Implants
    Manufacturer
    BIOMET3I
    Date Cleared
    2015-11-20

    (259 days)

    Product Code
    DZE
    Regulation Number
    872.3640
    Type
    Traditional
    Panel
    Dental Devices (DE)
    Reference & Predicate Devices
    K073368,K062044,K042637,K133049
    Why did this record match?
    Reference Devices :

    K073368, K062044, K042637

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

    The 3i T3® Short Implants are intended for surgical placement in the upper or lower jaw to provide a means for prosthetic attachment in single tooth restorations and in partially or fully edentulous spans with multiple single teeth utilizing delayed loading, or with a terminal or intermediary abutment for fixed or removable bridgework, and to retain overdentures.

    Device Description

    The 3i T3® Short Implants are manufactured from commercially pure titanium and feature a roughened apex and traditional OSSEOTITE® coronal surface. In addition, the implants are offered with or without a nano-scale discrete crystalline deposition (DCD®) calcium phosphate (CaP) surface treatment. The dental implants are basic screw-type designs available in parallel walled body geometries with an external hex connection for mating with associated Biomet 3t" external connection restorative components. The implants are offered in 5.0mm and 6.0 mm diameters and 5.0mm and 6.0mm lengths to accommodate varying patient anatomy. Size appropriate cover screws are provided with each implant.

    AI/ML Overview

    This document is a 510(k) summary for the 3i T3® Short Implants, which are dental implants. The goal of the 510(k) submission is to demonstrate substantial equivalence to legally marketed predicate devices, not to prove clinical effectiveness in a new study. Therefore, the information requested about acceptance criteria, clinical study design, and ground truth for an AI/device performance study is not directly applicable to this document.

    However, I can extract the information related to the non-clinical testing that was performed to support the substantial equivalence claim, which includes acceptance criteria and how "performance" was assessed in that context.

    Here's the breakdown based on the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance (Non-Clinical Testing)

    Test TypeAcceptance CriteriaReported Device Performance
    Fatigue Testing (ISO 14807:2007)The subject devices must meet pre-determined acceptance criteria. (Specific criteria not detailed in this summary.)The subject devices met the pre-determined acceptance criteria.
    Implant Surface Area AnalysisThe subject devices should be substantially equivalent to the predicate Bicon devices.The subject devices were substantially equivalent to the predicate Bicon devices.
    Bone to Implant Contact AnalysisThe subject devices should be substantially equivalent to the predicate Bicon devices.The subject devices were substantially equivalent to the predicate Bicon devices.
    Pullout Strength AnalysisThe subject devices should be substantially equivalent to the predicate Bicon devices.The subject devices were substantially equivalent to the predicate Bicon devices.
    MR Compatibility (RF heating, artifacts, displacement, torque)The device must pass testing in accordance with specified ASTM standards and FDA guidance for 1.5T and 3.0T MR environments.The Biomet 3i dental implants (inclusive of the subject devices) and restorative devices are considered to be MR Conditional in both 1.5T and 3.0T MR environments.

    Regarding the other requested points, as this is a 510(k) for a physical medical device (dental implant) and not an AI/software as a medical device, much of the requested information does not apply.

    • 2. Sample sized used for the test set and the data provenance: For the non-clinical tests, "worst-case" subject devices and worst-case predicate Bicon devices were used. Specific sample numbers are not provided in this summary. Data provenance is not applicable as this is laboratory testing of physical samples.
    • 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for non-clinical engineering tests (like fatigue or pullout strength) is based on physical measurements and established standards, not expert clinical consensus.
    • 4. Adjudication method for the test set: Not applicable for non-clinical engineering tests.
    • 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 not an AI-assisted device.
    • 6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable. This is not an AI-based device.
    • 7. The type of ground truth used: For the non-clinical tests, the "ground truth" is derived from physical measurements against established engineering standards (e.g., ISO 14807:2007, ASTM F2052-06e, etc.) and direct comparison to predicate device performance.
    • 8. The sample size for the training set: Not applicable. Non-clinical engineering tests do not typically involve a "training set" in the machine learning sense. The "training" for the device design would be part of standard engineering development and simulation, not a data-driven training set.
    • 9. How the ground truth for the training set was established: Not applicable.
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