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510(k) Data Aggregation

    K Number
    K090992
    Device Name
    WELL-PASTE
    Manufacturer
    VERICOM CO., LTD.
    Date Cleared
    2009-04-21

    (14 days)

    Product Code
    KIF
    Regulation Number
    872.3820
    Type
    Traditional
    Panel
    Dental
    Reference & Predicate Devices
    K032605
    Why did this record match?
    Device Name :

    WELL-PASTE

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

    Well-Paste™ is a biocompatible root canal sealer used for the temporary filling of root canals after endodontic surgery. Well-Paste™ can be used on its own and for vital pulpectomies in deciduous teeth.

    Device Description

    Well-Paste™ is a temporary root canal filling material after endodontic surgery as pulp capping, pulpotomy or apexification. It contains Calcium Hydroxide and Barium Sulfate mainly, so it shows excellent radiopacity. It also has high fluidity and excellent accessibility into the root canal. Well-Paste™ is premixed paste as a non-setting material and is very stable without any solidification or separation. And it is packaged in a convenient syringe with disposable tips, a plastic holder and disposable tip cap.

    AI/ML Overview

    This document is a 510(k) summary for a dental device, specifically a root canal filling material, and does not contain information about an AI/ML powered medical device. Therefore, it is not possible to answer the questions about acceptance criteria, study details, and AI/ML specific performance metrics as requested.

    The document describes the device, "Well-Paste™", its intended use, and its substantial equivalence to a predicate device ("Metapaste"). The review focuses on material characteristics (e.g., device design, appearance, main materials, physical properties like flow, film thickness, radiopacity, solubility, and disintegration) and biocompatibility, not on algorithm performance or clinical study outcomes involving human and AI comparison.

    Therefore, I cannot provide:

    1. A table of acceptance criteria and reported device performance related to AI.
    2. Sample size for a test set, data provenance, or ground truth details.
    3. Number or qualifications of experts for ground truth.
    4. Adjudication method for a test set.
    5. MRMC comparative effectiveness study results or effect size of AI improvement.
    6. Standalone algorithm performance.
    7. Type of ground truth used in an AI context.
    8. Sample size for a training set.
    9. How ground truth for a training set was established.
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