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510(k) Data Aggregation

    K Number
    K040148
    Date Cleared
    2004-04-22

    (90 days)

    Product Code
    Regulation Number
    878.4300
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    RESOLUTION HEMOSTASIS CLIPPING DEVICE

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

    Indicated for endoscopic clip placement within the gastrointestinal tract for the purpose of: endoscopic marking; hemostasis for mucosal/submucosal defects

    Device Description

    The ResolutionTM Hemostasis Clipping Device is a single-use pre-loaded mechanical clip and delivery system used for endoscopic clipping.

    AI/ML Overview

    Here's a breakdown of the acceptance criteria and study information for the Resolution™ Hemostasis Clipping Device based on the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance

    The provided document does not explicitly list quantitative acceptance criteria for the device. Instead, it relies on demonstrating "substantial equivalence" to a predicate device. The performance data section broadly states:

    Acceptance Criteria (Implicit)Reported Device Performance
    Similar technological characteristics to predicate deviceThe Resolution™ Hemostasis Clipping Device has similar technological characteristics to the predicate Olympus Clip Fixing devices.
    No new issues of safety or effectivenessThe results of performance testing show no new issues of safety or effectiveness.
    Substantially equivalent to predicate deviceThe device is substantially equivalent to the currently marketed Olympus Rotatable Clip Fixing Device (K013066).

    2. Sample Size Used for the Test Set and Data Provenance

    The document does not specify a sample size for a test set in a clinical or performance study involving human or animal subjects. The "performance data" mentioned refers to "technological characteristics and functional testing," which implies bench testing rather than a clinical trial. Therefore, information on data provenance (country of origin, retrospective/prospective) is also not available for a clinical test set.

    3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts

    This information is not provided in the document. Since there's no mention of a clinical test set with human data requiring expert review, there's no need for experts to establish ground truth in the context of this submission.

    4. Adjudication Method for the Test Set

    This information is not applicable as there is no mention of a human-reviewed test set or a clinical study that would require adjudication.

    5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

    A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done, or at least not reported in this document. The submission focuses on demonstrating substantial equivalence through technological and functional testing, not a comparative clinical study with human readers.

    6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

    This question is not applicable. The device is a physical clipping device, not an algorithm, so there is no "standalone" algorithm performance to evaluate.

    7. The Type of Ground Truth Used

    Given the nature of the device and the submission, the "ground truth" for the performance claims would primarily be based on engineering specifications and functional testing results rather than expert consensus, pathology, or outcomes data from a clinical trial. For example, successful deployment, clip retention, and mechanical strength would be evaluated against pre-defined engineering standards.

    8. The Sample Size for the Training Set

    This information is not applicable. The device is a physical medical device, not an AI/ML algorithm that requires training data.

    9. How the Ground Truth for the Training Set Was Established

    This information is not applicable as there is no training set for an AI/ML algorithm.

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