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510(k) Data Aggregation

    K Number
    K241005
    Date Cleared
    2025-01-02

    (265 days)

    Product Code
    Regulation Number
    876.4400
    Reference & Predicate Devices
    Why did this record match?
    Applicant Name (Manufacturer) :

    Jiangxi ZhuoRuiHua Medical Instrument Co., Ltd.

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

    The The Haemostasis Clips are indicated for endoscopic clip placement within the gastrointestinal tract for the purpose of:

    1. Endoscopic marking.
    2. Hemostasis for:
      Mucosal/sub-mucosal defects
    Device Description

    The Haemostasis Clips are sterile devices consisting of a pre-loaded, radiopaque, single-use, endoscopic clipping device consisting of two main components: the delivery system and the clip part. The delivery system consists of a handle assembly and delivery catheter assembly. The delivery system is constructed using stainless steel and polyester materials. The delivery system will allow for the device to rotate at the distal end. The delivery system is offered in 1650mm, 1950mm and 2350mm working lengths. The clip part consists of a stainless steel tightening capsule, movable pin and clip arms. The clip is deployed from the delivery system during use. The hemoclip jaws are engineered such that they can be opened and closed up to five times prior to deployment, aiding in repositioning of the clip at the lesion site. Re-opening, closing, and rotation capability may be limited by clinical circumstances and patient anatomy. There are no associated accessories included with this device.

    AI/ML Overview

    The provided text describes the 510(k) premarket notification for the "Haemostasis Clips" device. It outlines non-clinical testing performed to demonstrate substantial equivalence to a predicate device, rather than a study proving the device meets specific acceptance criteria based on clinical outcomes or a multi-reader, multi-case study in the traditional sense of AI/software device evaluation.

    The device is a medical instrument (Haemostasis Clips), not a software device or AI system, so many of the requested categories for AI/software evaluation (like MRMC studies, training set details, or ground truth establishment by experts for performance metrics) are not applicable. The evaluations are focused on the physical and performance characteristics of the clips themselves.

    Here's a breakdown of the information that can be extracted and which requested points are not applicable in this context:

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

    The document doesn't explicitly list numerical acceptance criteria for each test. Instead, it states that the tests were performed and the results "Pass" or "meet the requirement". For biocompatibility, specific outcomes like "Non-cytotoxic" are listed.

    TestReported Device Performance
    AppearancePass
    SizePass
    Open and ClosePass
    Rotation propertyPass
    Separation and clamping propertiesPass
    Clip release force testingPass
    Clip hardnessPass
    CorrosionPass
    Connection strength before clip releasedPass
    Breaking force of the delivery partPass
    Clamping force maintenancePass
    Chemical propertiesPass
    In vitro cytotoxicityNon-cytotoxic
    Skin sensitizationNon-sensitive
    Intracutaneous reactivityNon-irritation
    Acute systemic toxicityNon-acute systemic toxicity
    PyrogenNon-pyrogenic
    Hemolysis testNon Hemolysis reaction
    Sub chronic toxicityNon-Sub chronic toxicity
    Implantation effectNon-Implantation effect
    Sterility Assurance Level (SAL)10⁻⁶ demonstrated
    EO residualsMeets requirements
    Shelf-life3 years (based on accelerated aging)

    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 sizes for each of the bench or biocompatibility tests. It also does not mention data provenance as these are non-clinical (laboratory/bench) tests on manufactured devices, not data-driven studies from human subjects.

    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)

    Not applicable. These are non-clinical, bench and biocompatibility tests for a physical medical device. "Ground truth" in the context of expert consensus on medical images or clinical outcomes is not relevant here. The "ground truth" for these tests are objective, measurable physical and chemical properties and biological reactions determined by standardized test methods.

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

    Not applicable. This concept relates to human expert review and consensus, which is not part of the non-clinical bench or biocompatibility testing described.

    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

    Not applicable. The device is a physical medical instrument (Haemostasis Clip), not an AI/software device. Therefore, no MRMC study or evaluation of human reader improvement with AI assistance was performed or is relevant.

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

    Not applicable. The device is a physical medical instrument, not an algorithm.

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

    Not applicable. The "ground truth" for these tests are the inherent physical, chemical, and biological properties measured against established standards (e.g., ISO standards, "Pass" criteria for physical performance).

    8. The sample size for the training set

    Not applicable. This information pertains to machine learning models, which are not relevant to this device.

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

    Not applicable. This information pertains to machine learning models, which are not relevant to this device.

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