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510(k) Data Aggregation

    K Number
    K140368
    Date Cleared
    2014-04-29

    (74 days)

    Product Code
    Regulation Number
    886.1660
    Reference & Predicate Devices
    Why did this record match?
    Product Code :

    HKS

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

    The Sensor Medical Technology Family of Disposable Lenses are a family of diagnostic and therapeutic contact lenses used in the examination of the eye fundus, retina and iridocorneal and vitreous bodies and for the laser therapy of intraocular abnormalities.

    Device Description

    The Sensor Medical Technology Family of Disposable Lenses is a family of diagnostic and therapeutic contact lenses use for eve examination and therapy of intraocular abnormalities. The Family consists of 14 lenses - 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mirror Lens, 3 Mirror Lens, Iridotomy Lens, Capsulotomy Lens, Retina 90 Lens, Retina 165 Lens, Retina 180 Lens, Indirect Lenses: 90 D, 78 D, 60 D, 28 D and 20 D.
    The Family of Ophthalmic Lenses is designed around the classic Goldmann contact lens. Each model lens is of similar design, but provide different optical elements to provide excellent visualization of the ocular anatomical areas for the particular intended use. When used in conjunction with a slit lamp, the ophthalmic lenses provide a binocular and stereoscopic view of the specific optical region of the eye.
    The lenses are provided sterile for single use.

    AI/ML Overview

    The provided documentation describes a 510(k) premarket notification for the "Sensor Medical Technology LLC Family of Disposable Lenses." This submission primarily focuses on establishing substantial equivalence to a predicate device (Sensor Family of Ophthalmic Lenses, K102371) that was previously 510(k) exempt. The main difference between the new device and the predicate is that the new lenses are provided sterile for single use, whereas the predicate devices were reusable.

    Therefore, the performance data presented is focused on demonstrating that the disposable, sterile versions are as safe and effective as the reusable predicate, particularly regarding sterility and shelf life. This is not a study to demonstrate AI/algorithm performance in the typical sense of a diagnostic or predictive device with metrics like sensitivity, specificity, or AUC. Instead, it's a demonstration of product safety and efficacy through engineering and biological testing.

    Here's an analysis based on the provided text, highlighting the information (or lack thereof) for each requested point:


    Acceptance Criteria and Study for Sensor Medical Technology LLC Family of Disposable Lenses

    This submission is for a family of disposable ophthalmic lenses, not an AI or algorithm-based device. The "acceptance criteria" and "device performance" are therefore related to the physical and biological characteristics of the lenses, particularly regarding sterility and shelf life, rather than diagnostic accuracy or clinical effectiveness in the AI context.

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance CriteriaReported Device Performance
    Sterilization RequirementsMet the sterilization requirements specified in the sterilization validation protocol.
    Shelf LifeConfirmed a 24-month shelf life.
    Substantial EquivalenceDetermined substantially equivalent to the predicate device (K102371).

    2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

    The document does not specify the sample size for the sterility validation or shelf-life testing. These tests are typically performed on a statistically relevant number of product units from manufacturing lots, but the exact numbers are not reported in this summary. The provenance of the data (country of origin, retrospective/prospective) is also not mentioned, as these are technical validation studies performed by the manufacturer or contracted labs.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

    This is not applicable. The "ground truth" for sterility is determined by microbiological testing and for shelf life by accelerated or real-time aging studies and subsequent performance testing, not by expert human interpretation in a clinical context.

    4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

    This is not applicable. Adjudication methods like 2+1 or 3+1 are used for resolving discrepancies in expert interpretations (e.g., reading medical images). Here, the testing involves laboratory procedures for sterility and product stability, where "adjudication" in the human-reader sense does not apply.

    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. This is not an AI-assisted device. An MRMC study was not performed. The device is a physical ophthalmic lens.

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

    No. This is not an AI algorithm.

    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

    The "ground truth" for this device's performance validation is:

    • Sterility Validation: Microbiological test results (e.g., sterility test, bioburden, endotoxin testing) according to recognized standards (e.g., ISO 11135 or ISO 11137 for sterilization, ISO 11737 for bioburden).
    • Shelf Life Testing: Physical and functional integrity of the device and its packaging over time, confirmed by various tests (e.g., visual inspection, package integrity, functional performance, and sterility at different time points) after accelerated or real-time aging.

    8. The sample size for the training set

    This is not applicable. There is no "training set" as this is not an AI/machine learning device.

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

    This is not applicable as there is no training set.

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