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510(k) Data Aggregation

    K Number
    K152565
    Manufacturer
    Date Cleared
    2015-10-09

    (30 days)

    Product Code
    Regulation Number
    876.1500
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    This device is used during laparoscopy, creating a point of entry for laparoscopic instruments into the abdominal cavity.

    Device Description

    The Auto Suture™ Locking Trocar is a sterile, single-use device designed for primary punctures during laparoscopy.

    AI/ML Overview

    The provided document is a 510(k) summary for the Auto Suture™ Locking Trocar. It does not contain information about the acceptance criteria and study details as requested for a device involving AI or complex performance metrics. The device in question is a surgical trocar, which is a mechanical device, and the document focuses on demonstrating substantial equivalence to a predicate device through bench testing rather than detailed performance reporting against specific clinical acceptance criteria.

    Therefore, many of the requested sections about AI-specific criteria, human reader studies, ground truth establishment, sample sizes for training/test sets, and expert qualifications are not applicable or cannot be extracted from this document.

    Here's what can be extracted based on the information provided:

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

    The document states: "Bench Performance testing was conducted to demonstrate that the Auto Suture™ Locking Trocar is substantially equivalent to the predicate device and performs as intended."

    Specific quantitative acceptance criteria are not provided in this summary. The performance is described in terms of "substantial equivalence" to the predicate device and "performs as intended," without explicit metrics.

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

    • Sample Size for Test Set: Not specified.
    • Data Provenance: The testing appears to be "Bench Performance testing," implying laboratory-based testing rather than clinical data from human subjects. No information on country of origin or retrospective/prospective nature is provided, as it's not a clinical study.

    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. Ground truth, in the context of expert review, is typically relevant for interpretative devices (like imaging AI). For a mechanical surgical trocar, performance is assessed through engineering and biocompatibility testing, not expert consensus on interpretations.

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

    Not applicable, as it's not an interpretative study requiring adjudication.

    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. This device is a surgical trocar, not an AI-powered diagnostic or interpretive tool. No human reader studies or AI assistance are mentioned.

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

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

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

    For this type of device, the "ground truth" would be established by engineering specifications, safety standards (e.g., ISO 10993-1 for materials), and functional requirements met through mechanical testing. The document states "All components of the Auto Suture™ Locking Trocar are comprised of materials which are in accordance with ISO 10993-1," which refers to biocompatibility standards. Specific functional "ground truth" for dimensions, strength, seal integrity, etc., would be defined by internal specifications and predicate device performance, but not explicitly detailed in this summary.

    8. The sample size for the training set

    Not applicable. This is not a machine learning device that requires a training set.

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

    Not applicable. This is not a machine learning device.

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