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510(k) Data Aggregation

    K Number
    K202418
    Date Cleared
    2020-12-03

    (101 days)

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

    Magic UCLA Abutment System

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

    The Magic UCLA Abutment System is intended to replace missing teeth to restore chewing function. The Magic UCLA Abutment System can be placed in support of single or multiple-unit restorations including: cement retained, screw retained, and terminal or immediate abutment support for fixed bridgework. This system is for one or two stage surgical procedures. This system is intended for delayed loading.

    Device Description

    The Magic UCLA Abutment System is used with a dental implant to provide support to prosthetic restorations such as crowns, bridges, and overdentures in partially or fully edentulous patients. The system includes Magic UCLA Screw Retained Type (Hex, Non-Hex) and Magic UCLA Cement Retained Type (Hex, Non-Hex) abutments. The abutments are fabricated from Co-Cr-Mo Alloy with Poly Diacetate, provided non-sterile, and packaged separately. They must be sterilized before use. The abutments are not intended to be cast at angulation or placed to provide angular correction.

    AI/ML Overview

    The provided document is a 510(k) premarket notification for a dental device, the "Magic UCLA Abutment System." It primarily focuses on demonstrating substantial equivalence to previously cleared predicate devices rather than proving performance against specific acceptance criteria for a new AI/medical image analysis device.

    Therefore, this document does not contain the information required to answer your request about acceptance criteria and a study proving device performance for an AI/medical image analysis device.

    The relevant sections of your request (acceptance criteria, test set details, expert ground truth, MRMC study, standalone performance, training set details) are specific to the validation of AI/image analysis algorithms, which is not the subject of this 510(k) submission. This submission is for a physical dental implant component and leverages non-clinical testing performed on predicate devices to demonstrate equivalence.

    Here's a breakdown of why this document cannot fulfill your request:

    • No AI/Image Analysis: The "Magic UCLA Abutment System" is a physical dental implant component (abutment). There is no mention of it being an AI device or involving image analysis.
    • No Performance Study Against Acceptance Criteria (as you defined them): The document confirms "Non-clinical testing was not performed for the subject device" (page 9) because it's considered substantially equivalent to predicate devices based on material, manufacturing, and design similarities. The "tests" mentioned are biocompatibility, galvanic reaction, and sterilization tests, which are standard for physical medical devices and not related to AI performance metrics like sensitivity, specificity, F1-score, etc., nor do they involve human expert review for ground truth in the context of image analysis.
    • No Test/Training Sets: Since there's no AI component, there are no test or training sets of data/images to evaluate an algorithm's performance.
    • No Experts for Ground Truth (in the context of AI): The document doesn't discuss establishing ground truth by experts for image analysis.
    • No MRMC Study: An MRMC study is relevant for evaluating human reader performance with and without AI assistance. This is not applicable here.
    • No Standalone Performance: Standalone performance refers to an algorithm's performance without human intervention. This is not reported.

    In summary, the provided text describes a regulatory submission for a physical dental device, not an AI or image analysis device, and thus does not contain the information requested about acceptance criteria and performance studies typical for AI/medical imaging products.

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