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510(k) Data Aggregation

    K Number
    K040433
    Device Name
    SINON
    Manufacturer
    WAVELIGHT LASER TECHNOLOGIE AG
    Date Cleared
    2004-05-19

    (90 days)

    Product Code
    GEX
    Regulation Number
    878.4810
    Type
    Traditional
    Panel
    General & Plastic Surgery
    Reference & Predicate Devices
    K991285,K910422,K971814
    Why did this record match?
    Reference Devices :

    K991285, K910422, K9909002, K971814

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use
    1. In the Q-Switch Mode, for the cutting, vaporization, or ablation of soft tissue. This includes the removal of tattoos and treatment of benign pigmented lesions.
    2. In the Free-Running Mode, for the removal of unwanted hair in patients with Fitzpatrick skin types of I and II.
    Device Description

    The SINON is a 694 nm Ruby laser system which can be operated in two different modes. The quality-switch or Q-switch or QS Operating Mode is characterized by extremely short pulse widths (ns = 103s) and high peak power (MW = 106 W). The QS Operating Mode in the SINON is used for the removal of tattoos and treatment of pigmented lesions. The second mode is the Free-Running Operating Mode which is used for the removal of hair.

    AI/ML Overview

    The provided document, K040433 for the WaveLight Laser Technologie, AG SINON laser system, does not contain information about acceptance criteria or a study proving the device meets them.

    The document explicitly states "Performance Data None presented." in section 6. This means there are no details available within this submission regarding:

    • Specific acceptance criteria for device performance.
    • Any studies conducted to demonstrate the device meets such criteria.
    • Sample sizes for test sets or their provenance.
    • Information about experts, ground truth, or adjudication methods for any hypothetical test sets.
    • Any multi-reader multi-case (MRMC) comparative effectiveness studies.
    • Standalone algorithm performance.
    • Training set details or how their ground truth was established.

    This 510(k) submission primarily focuses on establishing substantial equivalence to predicate devices based on device description and intended use, rather than presenting detailed performance study data.

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