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510(k) Data Aggregation

    K Number
    K080603
    Manufacturer
    Date Cleared
    2008-08-05

    (155 days)

    Product Code
    Regulation Number
    868.5150
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Temena Nerve Location Needles are intended for use in regional anesthesia procedures, by anesthesiologists or other trained professionals, for the location of peripheral nerves with a nerve stimulator and/or with an ultrasound device.

    Device Description

    The Temena Hybrid and USC Nerve Location Needles include a stainless steel cannula and connections for anesthetic administration and with the Hybrid Needle a connection for nerve stimulation. The needle is available in a range of lengths and 21 or 22 AWG. The USC (uncoated version) may be used for echo nerve location and the Hybrid (coated version) for both stimulator and echo location.

    AI/ML Overview

    The provided text describes a 510(k) summary for the Temena Nerve Location Needles, which seeks substantial equivalence to predicate devices rather than proving performance against specific acceptance criteria through a clinical study with detailed statistical analysis. Therefore, much of the requested information cannot be directly extracted as it pertains to a different type of regulatory submission or study design.

    Here's an analysis based on the provided document:

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

    The document does not present a table of specific acceptance criteria. Instead, it relies on demonstrating substantial equivalence to existing predicate devices (Temena UPC Nerve Stimulator Needle K990100 and Havel Echostim Facit Tip K063380) by comparing key characteristics. The "reported device performance" is implicitly that it functions similarly to the predicate devices and the modifications (coating, surface for echo location) were confirmed with "acceptable results" under Design Controls. The table provided is a comparison chart of characteristics rather than an acceptance criteria table with performance metrics.

    CharacteristicTemena Hybrid and USC (New Device)Temena K990100 (Predicate 1)Havel K0633380 (Predicate 2)
    Nerve LocationYesYesYes
    Electro StimulationYesYesYes
    Echo LocationYesNoYes
    Sizes21-22 AWG21-30 AWG21 AWG
    Lengths35-150mm25-150mm40-150mm
    CoatingParylene (Nanolyene)TeflonNanoline
    Uncoated VersionYesNoYes
    PackagingSingle Teflon SealedSameSame
    SterilizationETOETOETO
    Regional AnesthesiaYesYesYes
    Anesthetic Admin. LineYesYesYes
    Needle TipBevelPencil Point (UPA) / Bevel (UPC)Bevel
    CoatingHybrid Yes, USC NoYesYes
    Tip RoughnessYesNoYes

    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 mentions that "Testing has been performed under Design Controls to confirm these changes with acceptable results." However, it does not specify the sample size for any test set, nor does it provide details on the data provenance (e.g., country of origin, retrospective or prospective). This information is typically found in detailed validation reports, which are not part of this 510(k) summary. Given the nature of a 510(k) for a Class II device like a needle, the "testing" likely refers to bench testing (e.g., electrical conductivity, mechanical strength, biocompatibility, coating adhesion, visualization properties for ultrasound) rather than a clinical study with 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)

    This information is not provided in the summary. For a device like this, the "ground truth" for performance would likely be established through objective measurements in laboratory or simulation settings, not typically by expert consensus on clinical data in the way an AI diagnostic device would.

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

    This information is not provided. Given the type of device and the "substantial equivalence" pathway, a formal adjudication process for a test set, as seen in clinical trials for diagnostic devices, is not describednor is it expected for this type of submission.

    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

    No MRMC comparative effectiveness study was done or reported. This device is a medical instrument (needle) for nerve location, not an AI diagnostic tool. Therefore, the concept of "human readers improve with AI" does not apply.

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

    Not applicable. This is a medical device (needle), not an algorithm or AI system.

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

    The document does not explicitly state the type of "ground truth" used. However, for a nerve location needle, the "ground truth" for its performance would likely be based on:

    • Physical and electrical properties: Bench testing for electrical impedance, mechanical strength, coating integrity, and ultrasonic visibility.
    • Biocompatibility: In vitro and/or in vivo testing (not described here but standard for implanted devices).
    • Functionality: Demonstration that the needle can deliver current for nerve stimulation and/or is visible under ultrasound as intended.

    These would be measured against engineering specifications and industry standards rather than human-derived ground truths like pathology or expert consensus.

    8. The sample size for the training set

    Not applicable. This is a physical medical device, not an AI model requiring a training set.

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

    Not applicable. This is a physical medical device, not an AI model.

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