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510(k) Data Aggregation

    K Number
    K202617
    Device Name
    Metapex Plus
    Date Cleared
    2021-04-14

    (217 days)

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

    Metapex Plus

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

    Metapex Plus is a calcium hydroxide paste with iodoform, used as a temporary root canal filling material. Application : Root canal filling material/Apexification and hard tissue formation/Apexogensis

    Device Description

    This product is a calcium hydroxide paste material that is temporarily filled into the root canal. It is pre-mixed in a syringe for convenient delivery in the root canal. When applied to root canal, it is alkaline by calcium hydroxide. The state of the treatment can be confirmed by the radiopacity of iodoform contained. Metapex Plus is used for patients of all ages requiring root canal treatment.

    AI/ML Overview

    The provided text describes the 510(k) premarket notification for "Metapex Plus," a root canal filling material. This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study proving a device meets specific acceptance criteria based on AI performance or diagnostic accuracy.

    Therefore, the provided input does not contain the information requested to describe acceptance criteria and a study proving a device meets them in the context of an AI/human reader performance study.

    The information in the document pertains to:

    • Device Type: A medical device (root canal filling material), not an AI-powered diagnostic tool.
    • Approval Type: 510(k) clearance, which focuses on substantial equivalence to a legally marketed predicate device, primarily through comparing technological characteristics and performance standards (like ISO 6876 for dental materials), rather than clinical efficacy trials or AI performance evaluations.
    • Testing: Non-clinical testing for performance (e.g., flowability, radiopacity) and biocompatibility, as well as shelf-life testing, all against established ISO standards for dental materials. There is no mention of a test set, training set, ground truth established by experts, or human reader performance studies.

    To answer your request, if this were an AI-powered diagnostic device, the following information would be expected but is NOT present in the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance: This would define metrics like sensitivity, specificity, AUC, or F-score and specify threshold values for acceptance.
    2. Sample Size and Data Provenance: For an AI study, this would detail the number of images/cases in the test set, their origin (e.g., country, hospital), and whether the data was collected retrospectively or prospectively.
    3. Number and Qualifications of Experts for Ground Truth: This would specify how many radiologists (or other medical specialists) with what level of experience established the ground truth.
    4. Adjudication Method: How discrepancies between experts were resolved (e.g., 2+1 agreement, majority vote).
    5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: Whether human readers were tested with and without AI assistance, and the effect size of the AI's improvement on human performance.
    6. Standalone Performance: The AI algorithm's performance without human interaction.
    7. Type of Ground Truth: How the definitive diagnosis was established (e.g., pathology, long-term outcomes, expert consensus).
    8. Training Set Sample Size: The number of cases used to train the AI model.
    9. Training Set Ground Truth Establishment: How the labels for the training data were determined.

    In summary, the provided FDA 510(k) clearance letter and summary discuss a dental material's substantial equivalence based on material properties and non-clinical testing against ISO standards, not a study evaluating the performance of an AI-powered diagnostic device or human-in-the-loop performance.

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