Search Filters

Search Results

Found 1 results

510(k) Data Aggregation

    K Number
    K213696
    Date Cleared
    2022-01-21

    (59 days)

    Product Code
    Regulation Number
    878.4400
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K193145

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

    The Ethicon Megadyne Electrosurgical Generator (ESU) is intended as a general-purpose electrosurgical generator designed to produce radio frequency (RF) current for cutting and coagulation to be delivered to target tissue through an accessory electrode during open and laparoscopic surgical procedures.

    Device Description

    The Ethicon Megadyne TM Electrosurgical Generator is a microprocessor controlled, isolated output, high frequency generator designed for use in cutting and coagulation of tissue. The generator has the ability to perform both monopolar cutting and coagulation and bipolar coagulation of tissue in a wide range of surgical applications.

    AI/ML Overview

    The Megadyne Electrosurgical Generator, a general-purpose electrosurgical generator designed to produce radio frequency (RF) current for cutting and coagulation of tissue during surgical procedures, underwent various performance tests to ensure its safety and effectiveness following design changes.

    1. Table of Acceptance Criteria and Reported Device Performance:

    The document primarily focuses on demonstrating substantial equivalence to a predicate device (K193145) rather than explicitly stating acceptance criteria and reporting performance for each individual criterion in a pass/fail format. However, based on the provided "Performance Data" and the "Comparison of Technological Characteristics" tables, the acceptance criteria implicitly involve demonstrating that the device's technical specifications and performance are either identical to the predicate or that any differences do not adversely affect safety and effectiveness.

    Here's a summary of the implied acceptance criteria and the device's reported performance:

    Acceptance Criteria (Implied)Reported Device Performance
    Biocompatibility: No direct or indirect patient contact.Biocompatibility testing not applicable as the device does not have any direct or indirect patient-contacting components.
    Electrical Safety: Compliance with IEC 60601-1 and 60601-2-2.Testing was completed in compliance with IEC standard 60601-1 (electrical safety) and IEC 60601-2-2 (electrosurgical generators).
    Electromagnetic Compatibility (EMC): Compliance with IEC 60601-1-2 and 60601-2-18.Testing was completed in compliance with IEC 60601-1-2 (EMC) and IEC 60601-2-18 (capacitive coupling for electrosurgical generators).
    Sterilization/Shelf-Life: Device to be non-sterile.The subject device is packaged and shipped non-sterile.
    Bench Testing (Thermal Effects on Tissue): Thermal effect for subject device not significantly different from predicate.Thermal effects on tissue were evaluated in comparison to the predicate. Testing was performed in triplicate at minimum, with default and maximum power settings for all Generator modes using corresponding devices. Image analysis with open-source software was used to measure thermal damage. Result: The thermal effect for the subject device is reported as "not significantly different" from that measured from the predicate device.
    Software Verification and Validation (V&V): Compliance with FDA guidance for "Major" level of concern software.Software validation and verification were completed following FDA's guidance: "General Principles of Software Validation," "Guidance for the Content of Premarket Submission for Software Contained in Medical Devices," and "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices." The recommended documentation for a "Major" level of concern software was provided.
    Technological Characteristics: Maintain similar operating principles, design, and performance as predicate, with justified differences.Most characteristics (Operating Voltage, Altitude/Pressure, Max Operating Duty Cycle, Current Rating, Power Consumption, Number of channels, Power Display Settings, Operating/Storage Conditions, Equilibration Time, Sterilization/Reprocessing, Cleaning, Operation and Service Manuals) are the same as the predicate.
    Differences: Maximum power output/setting of the monopolar mode decreased (except GEM mode), Auto-bipolar option removed, Single plate electrode accessories not compatible. These differences are implicitly accepted as the conclusion states substantial equivalence and no new questions of safety/effectiveness are raised.

    2. Sample size used for the test set and the data provenance:

    • Sample Size: For "Bench Testing (Thermal Effects on Tissue)," testing was performed "in triplicate at minimum". This indicates at least three repetitions for each combination of Generator mode and power setting. The document does not specify a total number of tissue samples or test runs beyond this.
    • Data Provenance: The data appears to be prospective bench testing conducted specifically for the purpose of this 510(k) submission to demonstrate the performance of the modified device. The document does not mention the country of origin of the data, but it is implied to be generated by the manufacturer or its contracted laboratories for the FDA submission.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

    • The document does not mention the use of experts to establish ground truth for the bench testing. The evaluation of thermal effects was done through "image analysis using open source image processing software." This suggests an objective, quantitative measurement rather than expert interpretation for the thermal effects study. For other tests like electrical safety and EMC, ground truth is established by adherence to specified standards.

    4. Adjudication method for the test set:

    • None specified. Given the nature of the tests (compliance with standards, objective thermal measurement via software), an adjudication method in the context of expert consensus or disagreement is not applicable or explicitly mentioned.

    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 study was done. This device is an electrosurgical generator, not an AI-powered diagnostic or assistive tool for human readers. Therefore, an MRMC study and analysis of AI-assisted human performance are not relevant to this submission.

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

    • Not applicable directly as an "algorithm only" study. The device itself is electro-mechanical, with embedded software. Software verification and validation (V&V) was performed to ensure the software functions correctly independently, but this is not a "standalone algorithm" performance in the sense of a diagnostic or predictive AI. The entire device's performance, including its software, was assessed.

    7. The type of ground truth used:

    • Compliance with International Standards: For electrical safety and EMC, the ground truth is defined by the requirements and test methods specified in the relevant IEC 60601 series standards.
    • Objective Measurement: For thermal effects on tissue, the ground truth was based on quantitative measurements of thermal damage obtained through image analysis software.
    • Manufacturer's Specifications/Design Intent: For other characteristics like operating parameters and device features, the ground truth is established by the device's design specifications and comparison to the predicate device's known characteristics.

    8. The sample size for the training set:

    • Not applicable. This submission is for an electrosurgical generator, which is not an AI/ML-based device that would typically involve a "training set" for model development. The software V&V confirms the performance of the embedded software, but it's not a machine learning model that learns from a training dataset.

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

    • Not applicable. As a training set is not relevant for this type of device, this question is not pertinent.
    Ask a Question

    Ask a specific question about this device

    Page 1 of 1