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510(k) Data Aggregation

    K Number
    K222891
    Date Cleared
    2023-05-25

    (244 days)

    Product Code
    Regulation Number
    872.3260
    Panel
    Dental
    Reference & Predicate Devices
    Why did this record match?
    Applicant Name (Manufacturer) :

    Guangzhou Beogene Biotech Co., Ltd

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

    The Dental Desensitizer is a colorless transparent gel that is applied to the sensitive part of the tooth to form a film, sealing the exposed dentin tubules and relieving dentin allergy. The product is used either by a dental professional in the dental office or provided to the patient for home treatment of dentin sensitivity.

    Device Description

    Dental Desensitizer is a sustained release, 3% potassium nitrate and 0.11% weight fluoride ion, viscous gel. This product is designed to be used together with a custom-fabricated tray.

    AI/ML Overview

    The provided text is a 510(k) Summary for a Dental Desensitizer. It outlines the premarket notification to the FDA for this device, claiming substantial equivalence to a predicate device, the UltraEZ Desensitizing Gel (K061438).

    However, this document does not contain information about the acceptance criteria and study proving a software or AI/ML-based device meets acceptance criteria.

    The document specifically states under section (9) "Clinical studies and tests performed": "Clinical studies and tests were not conducted."

    Instead, the submission relies on non-clinical studies and tests, focusing on the physical, chemical, and biological properties of the dental desensitizer product itself, and its comparison to the predicate device.

    Therefore, it is not possible to provide the requested information regarding AI/ML device acceptance criteria and study details based on the provided text.

    The document details relate to a material/chemical product (a dental gel), not a software or AI/ML device. The questions in the prompt (e.g., sample size for test set, number of experts for ground truth, MRMC study, standalone algorithm performance, training set details) are specific to the evaluation of AI/ML-based medical devices, which are not applicable here.

    In summary, the provided context does not offer the necessary information to answer the questions about AI/ML device acceptance criteria and proving device performance.

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