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510(k) Data Aggregation

    K Number
    K183126
    Date Cleared
    2019-05-08

    (176 days)

    Product Code
    Regulation Number
    878.4400
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Flexible Argon Electrode is intended to be used in open or laparoscopic and thoracoscopic surgical procedures (general, neurosurgical, gynecologic) where argon-enhanced monopolar coagulation is desired.

    The Retractable Argon Electrode is intended to be used in laparoscopic and thoracoscopic surgical procedures (general, neurosurgical, gynecologic) where standard monopolar electrosurgery (cutting and coagulation) or argon-enhanced monopolar coagulation is desired.

    The New Deantronics Argon Electrodes have to be attached to the argon handset and are the electrosurgical electrodes applied in electrosurgical procedures.

    Device Description

    The New Deantronics Argon Electrode is designed to connect to a third-party argon handset. The electrosurgical energy is conducted from the argon handset, through the electrode to the electrosurgical target site, as is the argon gas flow. When the device is activated, the argon gas will be transformed into a plasma state flowing out of the electrode. By directly or indirectly contacting the energized electrode with the target tissue, the New Deantronics Argon Electrode can facilitate argon-enhanced coagulation, as well as standard electrosurgical cut and coagulation, as indicated.

    AI/ML Overview

    The provided text describes the New Deantronics Argon Electrodes, which are electrosurgical accessories, and asserts their substantial equivalence to a predicate device based on various comparisons and non-clinical testing. However, the document does not contain the detailed information required to answer all parts of your request, particularly regarding specific acceptance criteria metrics and a human-focused study design.

    Here's a breakdown of what can be extracted and what is missing:

    1. Table of acceptance criteria and the reported device performance

    The document mentions "pre-defined acceptance criteria" but does not provide a specific table outlining those criteria or the reported performance against them for functionality, safety, or clinical effectiveness. The only specific performance data mentioned related to criteria is:

    Acceptance CriteriaReported Device Performance
    High Frequency (HF) breakdown withstand voltage13.5kV peak to peak
    Main Frequency (60Hz) breakdown withstand voltage5.2kV RMS AC

    Beyond these electrical safety parameters, the document states: "Electrical safety testing and bench testing has demonstrated that the performance of New Deantronics Argon Electrodes meet the requirements of its pre-defined acceptance criteria and intended uses." This is a general statement rather than specific data.

    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 "electrical safety testing and bench testing" but does not specify the sample size for the test sets used in these non-clinical tests. It also does not mention the data provenance or whether the tests were retrospective or prospective. Given that it's bench testing, it's typically conducted in a controlled lab environment.

    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 applicable as the document describes non-clinical "electrical safety testing and bench testing," not a study involving human interpretation or expert ground truth establishment for a test set.

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

    This information is not applicable for the same reason as point 3.

    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

    The document describes development and testing of an "Electrosurgical accessory" (Argon Electrodes) and explicitly states it conducted "electrical safety testing and bench testing." It does not mention a MRMC comparative effectiveness study involving human readers or AI. Therefore, there is no effect size related to human readers improving with or without AI assistance.

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

    The document details "electrical safety testing and bench testing" of the device itself (Argon Electrodes), which can be considered a form of standalone performance evaluation for the device's physical and electrical characteristics. However, it's not an "algorithm only" standalone performance as the device itself is a physical medical instrument, not software.

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

    For the electrical safety and functional bench testing, the "ground truth" would be established by engineering specifications, industry standards (e.g., IEC 60601 series), and controlled measurements using calibrated equipment, not expert consensus, pathology, or outcomes data which are typically associated with clinical or diagnostic studies.

    8. The sample size for the training set

    This information is not applicable. The device is an electrosurgical accessory, not an AI/ML algorithm that requires a "training set." The testing described is for physical product performance, not algorithm training.

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

    This information is not applicable for the same reason as point 8.

    In summary:

    This document focuses on regulatory clearance for an electrosurgical accessory based on its physical properties and electrical safety, largely through comparison to a predicate device and non-clinical bench testing. It does not involve AI, human reader studies, or complex clinical ground truth assessments as would be relevant for diagnostic imaging or similar AI-driven medical devices.

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