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510(k) Data Aggregation

    K Number
    K972646
    Date Cleared
    1997-08-21

    (37 days)

    Product Code
    Regulation Number
    878.4022
    Reference & Predicate Devices
    N/A
    Why did this record match?
    Device Name :

    AD-HEAL HYDROGEL DRESSING, VARIOUS SIZES

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

    This hydrogel dressing is a sterile wound dressing for the management of partial thickness wounds, first and second degree burns, donor sites and superficial injuries. Ad-Heal™ Bydrogel Dressing is indicated for: First and Second Degree Burns Partial Thickness Wounds Donor Sites Pressure Ulcers Abrasions Note: Ad-Healm is not indicated for full thickness or infected wounds or for third degree burns. This is a disposable, short term (less than 72 hours between changes) device for single patient use

    Device Description

    This hydrogel dressing is a sterile wound dressing for the management of partial thickness wounds, first and second degree burns, donor sites and superficial injuries. This is a disposable, short term (less than 72 hours between changes) device for single patient use

    AI/ML Overview

    This is a medical device approval letter from the FDA for the Ad-Heal™ Hydrogel Dressing. The letter states that the device is substantially equivalent to legally marketed predicate devices. It does not contain information about acceptance criteria or a study proving the device meets those criteria. The letter primarily focuses on regulatory approval, labeling limitations, and general controls provisions of the Federal Food, Drug, and Cosmetic Act.

    Therefore, I cannot provide the requested information based on the provided text. The document does not contain details regarding:

    1. A table of acceptance criteria and reported device performance.
    2. Sample size used for a test set or data provenance.
    3. Number and qualifications of experts for ground truth.
    4. Adjudication method.
    5. Multi-reader multi-case (MRMC) comparative effectiveness study.
    6. Standalone algorithm performance.
    7. Type of ground truth used.
    8. Sample size for the training set.
    9. How ground truth for the training set was established.
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