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510(k) Data Aggregation

    K Number
    K982689
    Date Cleared
    1998-10-23

    (81 days)

    Product Code
    Regulation Number
    886.1120
    Reference & Predicate Devices
    N/A
    Why did this record match?
    Device Name :

    WINSTATION RETINAL IMAGER

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

    The WinStation Retinal Imager™ has the same intended use as other fundus cameras. It is specifically used to produce color or black and white images of the retina (fundus imaging) and anterior segment (slit lamp imaging), and fluorescein and ICG angiographic images in a non-invasive manner.

    Device Description

    Not Found

    AI/ML Overview

    I am sorry, but the provided text does not contain the acceptance criteria and study details you've requested regarding a medical device. The document is a 510(k) clearance letter from the FDA for the "WinStation Retinal Imager," dated October 23, 1998. It states that the device is substantially equivalent to legally marketed predicate devices and outlines the indications for use.

    To answer your questions, I would need a different document, such as a summary of safety and effectiveness (SSE), a clinical study report, or similar regulatory submission details that would include:

    1. Acceptance criteria and device performance: These are typically defined metrics (e.g., sensitivity, specificity, accuracy) that the device must meet, along with the reported performance values from a study.
    2. Sample size and data provenance: Details about the number of cases/patients used in testing and where the data came from (e.g., country, retrospective/prospective).
    3. Number and qualifications of experts for ground truth: Information about the specialists who established the correct diagnoses or measurements, including their experience levels.
    4. Adjudication method for ground truth: How disagreements among experts were resolved (e.g., majority vote, senior expert decision).
    5. MRMC comparative effectiveness study: If human reader performance with and without AI assistance was evaluated, and the effect size measured.
    6. Standalone performance: The performance of the algorithm without human intervention.
    7. Type of ground truth: How the true status of a condition was determined (e.g., pathology, clinical follow-up, expert consensus).
    8. Training set sample size: The number of data points used to train the AI model.
    9. Ground truth establishment for training set: How the correct labels were assigned to the training data.

    The current document only provides the FDA clearance, trade name, regulatory class, product code, and a general statement of indications for use, without any performance data or study design details.

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