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510(k) Data Aggregation

    K Number
    K191033
    Manufacturer
    Date Cleared
    2019-08-05

    (109 days)

    Product Code
    Regulation Number
    N/A
    Panel
    Dental
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The LunaGuard™ Nighttime Dental Protector is indicated for use in protecting the teeth and reducing damage caused by bruxism or nighttime grinding.

    Device Description

    The LunaGuard™ Nighttime Dental Protector is an over-the-counter, flexible and moldable one-piece dental guard consisting of a lightweight polycaprolactone (PCL) plastic material can diffuse and absorb grinding forces, along with the microperforations in the mouthguard, to protect the teeth from the grinding and clenching associated with bruxism.

    When in place over the upper teeth, the guard maintains separation between upper and lower teeth, reducing noise and damage associated with teeth grinding. It is designed to fit around the teeth with no or minimal space in order to form a barrier between the upper and lower teeth. The lightweight, thin design allows for comfort while sleeping, and does not impede speaking, drinking, or breathing.

    The LunaGuard™ material is self-fit by the user using the "boil and bite" method. Once submerged in hot but not boiling water, the dental guard turns clear when it reaches 160 degrees Fahrenheit (71.1 degrees Celsius), indicating readiness to mold. At this point, the user shapes the dental guard around their upper teeth for a snug and user-specific fit.

    AI/ML Overview

    The provided text is a 510(k) Premarket Notification from the FDA for a device called "LunaGuard Nighttime Dental Protector". This document primarily focuses on establishing substantial equivalence to a predicate device, rather than proving the device meets specific performance acceptance criteria for an AI/ML-based medical device.

    Therefore, much of the requested information about acceptance criteria, specific performance metrics (like sensitivity, specificity, or AUC), sample sizes for test sets, expert ground truth establishment, MRMC studies, standalone performance, and training set details, is not present in the provided document. This type of information is typically required for AI/ML device submissions where the algorithm's performance is the primary focus of review.

    Here's a breakdown of what can be extracted and what is explicitly unobtainable from the provided text:

    Unobtainable Information (due to the nature of this 510(k) for a physical device, not an AI/ML algorithm):

    • A table of acceptance criteria and the reported device performance: This document doesn't define AI-specific acceptance criteria or report performance metrics like sensitivity, specificity, or AUC. The performance data discussed is related to physical properties and biocompatibility.
    • Sample sizes used for the test set and the data provenance: Not applicable in the context of an AI/ML test set. The "test set" here refers to physical samples for material testing.
    • Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an AI/ML device would involve expert labeling of medical images/data. Here, "ground truth" for the physical properties would be lab measurements.
    • Adjudication method for the test set: Not applicable.
    • 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, as this is not an AI-assisted device.
    • If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable, as it's a physical device.
    • The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For physical testing, ground truth refers to validated laboratory measurements and standards (e.g., ISO 10993 for biocompatibility).
    • The sample size for the training set: Not applicable (no AI/ML training set).
    • How the ground truth for the training set was established: Not applicable.

    Information that can be extracted or inferred from the document:

    The document describes the acceptance criteria and supporting study in terms of substantial equivalence for a physical device, not an AI/ML algorithm.

    • Device Name: LunaGuard™ Nighttime Dental Protector
    • Predicate Device: Pro Tech Dent (K121272), manufactured by Akervall Technologies Inc.
    • Indications for Use: The LunaGuard™ Nighttime Dental Protector is indicated for use in protecting the teeth and reducing damage caused by bruxism or nighttime grinding. (Identical to the predicate device).

    Summary of "Acceptance Criteria" and "Device Performance" (in the context of a physical device's 510(k) ):

    The "acceptance criteria" here are implicitly tied to demonstrating that the LunaGuard™ is substantially equivalent to the predicate device in terms of:

    1. Intended Use and Indications for Use: Identical.
    2. Design and Materials: Similar (both polycaprolactone (PCL) plastic, 1.6 mm thickness).
    3. Method of Action and Principles of Operation: Similar (over-the-counter re-moldable dental guards, non-sterile, single user, boil-and-bite).
    4. Physical Properties: Equivalent as demonstrated by comparative testing.
    5. Biocompatibility: Device found biocompatible for its intended use.

    Study Proving Device Meets "Acceptance Criteria" (i.e., Substantial Equivalence):

    The studies conducted were non-clinical performance testing and biocompatibility testing.

    • Type of Study: Comparative performance testing and biocompatibility assessment.
    • Sample Size Used for Testing: The document does not specify the exact number of units or material samples used for the physical and biocompatibility tests. It broadly states "The physical properties of the LunaGuard™ device were tested against those of the predicate."
    • Data Provenance: The tests were performed in a laboratory setting for the purpose of this submission. The origin of the raw materials would be global, but the testing itself would be controlled by the manufacturer/testing labs. The document doesn't specify if this was retrospective or prospective testing, but for a 510(k), it's typically prospective testing of the manufactured product.
    • Ground Truth for Physical Testing: The "ground truth" for these tests would be the established scientific methods and standards (e.g., ISO 10993 for biocompatibility, and material characterization techniques like density, hot stage optical microscopy, hardness, melt flow rate, and tensile testing). The "truth" is the measured value compared against acceptable ranges or values of the predicate.
    • Qualifications of Experts for Testing: Not explicitly stated, but assumed to be qualified laboratory personnel following standard operating procedures for material and biocompatibility testing.
    • Adjudication Method: Not applicable for material testing; results are quantitative measurements.

    Biocompatibility Testing Details:

    • Standard: ISO 10993-1: 2018, Biological evaluation of medical devices – Part 1: Evaluation and testing within a risk management process.
    • Device Categorization: Surface device, contacts intraoral (mucosal, gingival, palatal) surfaces for limited contact.
    • Result: Results show that the device is biocompatible for its intended use.

    Non-Clinical Comparative Testing Details:

    • Comparison: LunaGuard™ vs. ProTech Dent (predicate).
    • Tests Performed: Density, hot stage optical microscopy, hardness, melt flow rate, and tensile testing.
    • Result: Found to be equivalent with respect to these properties.

    Conclusion:

    The provided document describes the regulatory submission for a physical medical device. The "acceptance criteria" and "study" refer to demonstrating substantial equivalence to a predicate device through material characterization and biocompatibility testing, not an AI/ML algorithm's diagnostic performance. Therefore, most of the detailed questions regarding AI/ML device testing are not applicable to this specific submission type.

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