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510(k) Data Aggregation

    K Number
    K013941
    Manufacturer
    Date Cleared
    2002-02-27

    (90 days)

    Product Code
    Regulation Number
    878.4810
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    PULSION FS LASER KERATOME

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

    The Pulsion™ FS is an ophthalmic surgical laser indicated for use in the creation of a corneal flap in patients undergoing LASIK surgery or other treatment requiring initial lamellar resection of the cornea.

    Device Description

    The Pulsion FS Laser is a precision ophthalmic surgical laser designed for use in performing lamellar corneal resections. The cutting action of the PULSION FS Laser is achieved through precise individual micro-photodisruptions of tissue, created by tightly focused ultrashort pulses which are delivered through a disposable applanation lens while fixating the eye under very low vacuum.

    AI/ML Overview

    The provided text is a 510(k) summary for the Pulsion FS Laser, which is an ophthalmic surgical laser. The document claims substantial equivalence to a predicate device (Pulsion FS Laser with 510(k) K993153). While it states that the device has undergone testing and complies with safety standards, and performs equivalently in "extensive ex vivo and in vivo studies," it does not provide acceptance criteria or a detailed study report that proves the device meets specific performance criteria.

    Therefore, most of the requested information cannot be extracted directly from the provided text.

    Here's an attempt to answer based on the available information, with notes where information is missing:


    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance CriteriaReported Device Performance
    Not specified in the document"found to perform equivalently to the predicate laser keratome, with respect to the creation of corneal resections in extensive ex vivo and in vivo studies."

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

    • Sample size: Not specified. The document only mentions "extensive ex vivo and in vivo studies."
    • Data provenance: Not specified (e.g., country of origin, retrospective or prospective).

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

    • Number of experts: Not specified.
    • Qualifications of experts: Not specified.

    4. Adjudication method for the test set

    • Adjudication method: Not specified.

    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

    • This device is a surgical laser, not an AI-assisted diagnostic tool. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not applicable and was not performed.

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

    • This device is a surgical laser, not an algorithm. Therefore, a standalone algorithm performance study is not applicable. The device's performance relates to its physical action of creating corneal resections.

    7. The type of ground truth used

    • The document mentions "creation of corneal resections." The ground truth would likely involve measurement of the characteristics of the corneal resections created by the laser (e.g., flap thickness, diameter, smoothness, side-cut angle, tissue integrity) in both ex vivo and in vivo models. However, the specific type of measurement or gold standard definition is not detailed.

    8. The sample size for the training set

    • This documentation pertains to a surgical device, not a machine learning model that has a "training set." Therefore, the concept of a training set sample size is not applicable.

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

    • As a surgical device, there is no "training set" in the context of machine learning. The device's performance is established through direct physical testing and comparison to a predicate device.

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