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510(k) Data Aggregation

    K Number
    K142759
    Manufacturer
    Date Cleared
    2015-01-13

    (110 days)

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

    PK Cutting Forceps for ESG-400 Workstation

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

    The PK Cutting Forceps are indicated for electrosurgical coagulation, mechanical cutting, and grasping of tissue during the performance of laparoscopic and open general surgical procedures when used in conjunction with the general purpose ESG-400 workstation.

    Device Description

    The PK Cutting Forceps are indicated for electrosurgical coagulation, mechanical cutting, and grasping of tissue during the performance of laparoscopic and open general surgical procedures when used in conjunction with the general purpose ESG-400 workstation. The device is a single use, sterile accessory, to be used in conjunction with the bipolar outputs of the Olympus workstation with PK software (ESG-400).

    The proposed device is a modification of the Gyrus HALO PKS Cutting Forceps and includes an improved handle and improved jaw performance. The device handle has been redesigned with internal changes to improve ease of use. The jaws are identical as those found on the predicate Gyrus HALO PKS Cutting Forceps except that coatings have been added, and a mechanical bridge has been added inside the shaft.

    AI/ML Overview

    The provided text is a 510(k) summary for the PK Cutting Forceps for the ESG-400 workstation. It outlines the device, its intended use, and substantial equivalence to a predicate device, but it does not contain acceptance criteria or a study that specifically proves the device meets such criteria in the context of AI/algorithm performance.

    The document describes performance testing for the physical device itself (electrical, mechanical, functional, and preclinical ex-vivo tissue evaluations) to ensure it functions as intended and is substantially equivalent to a predicate device. This is typical for a medical device clearance, but not for an AI/algorithm-driven device's performance metrics.

    Therefore, many of the requested items related to "device performance" in the context of AI/algorithm criteria (such as sample sizes for test sets, data provenance, expert ground truth, adjudication methods, MRMC studies, standalone performance, and training set information) cannot be extracted from this document as it's not relevant to the type of device being described.

    However, I can provide what is available regarding general performance and testing, interpreting "acceptance criteria" as general performance requirements and "reported device performance" as the results of the non-clinical and preclinical testing.

    Here's a breakdown of the information that can be extracted or inferred from the document:

    1. Table of Acceptance Criteria and Reported Device Performance:

    The document doesn't explicitly state quantitative acceptance criteria in a table format for specific performance metrics like sensitivity/specificity. Instead, it states that the device was tested to perform as intended and meet design specifications, as well as demonstrate substantial equivalence to the predicate device.

    Performance AreaGeneral "Acceptance Criteria" (Implied)Reported Device Performance
    BiocompatibilityMeet ISO 10993-1 guidelines for external communicating, tissue/bone/dentin device for limited exposure (
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