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510(k) Data Aggregation

    K Number
    K060869
    Date Cleared
    2006-05-11

    (42 days)

    Product Code
    Regulation Number
    886.1850
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    RS-1000 ZOOM SLITLAMP WITH OPTION

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

    The RS-1000 Zoom Slitlamp with option is intended for use in eye examination of the anterior eye segment, from the cornea epithelium to the posterior capsule. It is used to aid in the diagnosis of diseases or trauma which affect the structural properties of the anterior eye segment.

    Device Description

    The RS-1000 Zoom slitlamp microscope consists of a microscope with a halogen lamp which illuminates the inner eye for examination with an optional attachment for photographic or video capabilities and an illumination power supply.

    AI/ML Overview

    The provided documents describe a 510(k) premarket notification for the RS-1000 Zoom Slitlamp with option. This device is an AC-powered slitlamp biomicroscope intended for eye examination. The acceptance criteria and the study proving it meets these criteria are outlined in the "Performance testing" section.

    Here's an analysis of the requested information:

    1. A table of acceptance criteria and the reported device performance:

    Acceptance CriteriaReported Device Performance
    Meet safety requirements of standard IEC 60601-1Met all safety requirements of IEC 60601-1
    Meet electromagnetic compatibility requirements of standard IEC 60601-1-2Met all electromagnetic compatibility requirements of IEC 60601-1-2
    Substantially equivalent to predicate devices with respect to intended uses, technological characteristics, and safety and effectivenessDemonstrated equivalence to predicate devices (Marco Ophthalmic branded Ultra G5 zoom slitlamp, K930438)

    2. Sample size used for the test set and the data provenance:

    The document describes performance testing in accordance with IEC standards, which typically involves testing of the device itself rather than a test set of patient data. Therefore, there is no sample size of patient data mentioned for a test set, nor any information about data provenance (country of origin, retrospective/prospective). The testing focused on the device's electrical, mechanical, and electromagnetic properties.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

    This information is not applicable as the study did not involve establishing ground truth from patient data with expert readers. The testing was focused on engineering and performance standards for the medical device itself.

    4. Adjudication method for the test set:

    This information is not applicable as there was no test set involving human interpretation of medical images or data requiring adjudication.

    5. 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:

    No MRMC study was done, and therefore, no effect size of human reader improvement with AI assistance is mentioned. This device is a diagnostic illumination microscope and does not incorporate AI.

    6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

    This information is not applicable. The device is an optical instrument, not an algorithm.

    7. The type of ground truth used:

    The "ground truth" for this device's performance assessment appears to be the established technical and safety standards (IEC 60601-1 and IEC 60601-1-2) themselves. Compliance with these standards indicates the device performs as expected for its intended use. Equivalency to the predicate device also serves as a comparative "ground truth" for its clinical utility.

    8. The sample size for the training set:

    The document does not describe a "training set" in the context of machine learning or AI. The performance testing was on the manufactured device itself.

    9. How the ground truth for the training set was established:

    This information is not applicable as there was no training set in the context of machine learning or AI.

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