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510(k) Data Aggregation

    K Number
    K213483
    Date Cleared
    2022-04-26

    (179 days)

    Product Code
    Regulation Number
    876.1500
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    SnapLoc Wire Lock

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

    This device is an accessory to be used with endoscopic biliary and pancreatic devices to lock the wire guide(s) in place and to prevent reflux of bodily fluids during ERCP

    Device Description

    The SnapLoc Wire Lock device is an accessory that is clipped onto the outside of a Duodenoscope during Endoscopic Retrograde Cholangio-Pancreatography (ERCP) procedures. Its purpose is to help control the wire guides during a procedure, to enable ERCP devices (catheters, baskets, balloons, stents, etc) to pass through the device and to prevent the backflow of bodily fluids during procedures. The SnapLoc wire lock contains features to aid in wire control. The wire is locked into place by simply sliding the wire into one of the locking notches located on the left and right sides of the wire is unlocked by lifting the wire out of the wire locking notch. For control, the proximal end of the wire can be secured by inserting it into one of the docking channels. A silicone seal located between the molded components helps reduce leakage from the duodenoscope. For ease of connection, the wire lock attaches to the duodenoscope by sliding laterally onto the port until secure. The SnapLoc will be sold in three sizes to be compatible with the most commonly used endoscopes from Olympus. Fuji, and Pentax.

    AI/ML Overview

    The provided text does not contain detailed acceptance criteria or results from a study that proves the device meets those criteria. However, it does outline the types of performance testing conducted to demonstrate that the SnapLoc Wire Lock meets its design input requirements and is substantially equivalent to its predicate device.

    Here's an overview of the information that can be extracted from the text regarding the performance testing:

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

    The document lists performance tests conducted, but it does not provide specific numerical acceptance criteria or the reported performance data. It generally states that the tests demonstrated conformance to design input requirements and substantial equivalence.

    Here's a table based on the types of tests performed, but without specific numerical acceptance criteria or results:

    Acceptance Criterion (Inferred from Test)Reported Device Performance
    Force to Connect to DuodenoscopeConformed to design input requirements
    Leakage Rate At Scope SealConformed to design input requirements
    Force to Advance Device Through WirelockConformed to design input requirements
    Ability to Lock WireguideConformed to design input requirements
    Leakage Rate at Accessory SealConformed to design input requirements
    Force to Remove Catheter During Short Wire ExchangeConformed to design input requirements
    Translation Distance of Wire in Locked PositionConformed to design input requirements
    Force to Detach from DuodenoscopeConformed to design input requirements
    Joint Strength during Short Wire Exchange/Stent UseConformed to design input requirements
    BiocompatibilityDemonstrated no new questions of safety or effectiveness
    Shelf-life (1 year) after accelerated agingDemonstrated no significant material change

    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 states: "Where applicable, each test was performed on each variant of the SnapLoc device based on endoscope compatibility (Olympus, Fuji, Pentax)." It also mentions "representative devices" for accelerated aging testing.

    • Sample Size: Not explicitly stated as a number, but implies at least one device per variant (Olympus, Fuji, Pentax) for each test.
    • Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). It appears to be prospective testing conducted by the manufacturer as part of the 510(k) submission.

    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 type of information is not applicable as the testing described is primarily mechanical and material performance testing, not a clinical study involving human expert interpretation for ground truth.

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

    This is not applicable for the type of performance testing described. Adjudication methods are typically used in clinical studies where expert consensus is needed to establish ground truth from ambiguous data.

    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 indication that an MRMC study was conducted. The device is a mechanical accessory, not an AI-powered diagnostic tool.

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

    This is not applicable as the device is a mechanical accessory, not an algorithm.

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

    For the performance tests listed, the "ground truth" is derived from engineering specifications, material science principles, and functional requirements (e.g., a wire should lock, there should be no leakage, specific force thresholds should be met). It's not based on expert consensus, pathology, or outcomes data in the clinical sense.

    8. The sample size for the training set

    This is not applicable as the device is not an AI/machine learning device that requires a "training set."

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

    This is not applicable as the device is not an AI/machine learning device.

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