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510(k) Data Aggregation

    K Number
    K121833
    Manufacturer
    Date Cleared
    2012-07-16

    (24 days)

    Product Code
    Regulation Number
    876.5895
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    OSTOMY IRRIGATION SET

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

    The Irrigation Set is indicated for people with a stoma who want to irrigate.

    Device Description

    The Irrigation Set is a Class II device, consisting of a single-use sleeve; a water bag (500-2000 ml) to hold water, a regulator where the paddle wheel that is integrated in the regulator gives visual assurance that water is entering the colon, a tube with a cone-shaped tip, a press plate and belt which are reusable devices. The Irrigation Set is provided with a nylon storage case.

    AI/ML Overview

    The provided text describes the 510(k) summary for the Irrigation Set, a Class II medical device. It focuses on demonstrating substantial equivalence to a predicate device (K840268) based on design, intended use, and functional performance.

    Here's an analysis of the provided text in relation to your questions:

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

    Based on the provided text, the acceptance criteria and performance are listed as "functional performance and biocompatibility testing" with "acceptable results". More specific details are provided for the bench tests.

    Acceptance Criteria CategorySpecific Test / Performance MetricReported Device Performance
    Functional PerformanceAccuracy of Volume IndicatorAcceptable results
    Freedom from LeakageAcceptable results
    Flow ControllerAcceptable results
    Hanging StrengthAcceptable results
    BiocompatibilityBiocompatibility testingAcceptable results

    2. Sample sized 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 used for the test set (bench tests). It also does not mention the country of origin of the data or whether the tests were retrospective or prospective.

    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 information is not applicable and not provided. The testing involved in-vitro (bench) tests, not clinical evaluations that would require expert ground truth assessment.

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

    This information is not applicable and not provided. Adjudication methods are typically used in clinical studies involving interpretation by multiple experts, which is not the case for these bench tests.

    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

    There is no mention of an MRMC comparative effectiveness study, AI, or human readers in the provided text. The device is an "Irrigation Set" and the tests are non-clinical in nature.

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

    There is no mention of any algorithm or AI in the provided text. The device is a physical medical device.

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

    The concept of "ground truth" as typically used in AI/diagnostic studies is not applicable here. The "ground truth" for these non-clinical tests would be the established engineering specifications and scientific principles governing the performance of the device's components (e.g., a volume indicator's accuracy is measured against a known standard, leakage is determined by the absence of fluid escape, etc.). The study relies on objective physical measurements and established biocompatibility testing protocols.

    8. The sample size for the training set

    There is no mention of a "training set" in the provided text. This concept is relevant to machine learning or AI device development, which is not described here.

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

    Not applicable, as no training set is mentioned or relevant to the described device and testing.

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