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510(k) Data Aggregation

    K Number
    K172016
    Date Cleared
    2017-10-03

    (92 days)

    Product Code
    Regulation Number
    878.5000
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    Force Fiber Fusion Suture

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

    Force Fiber Fusion™ Suture is indicated for use in approximation and/or ligation of soft tissues, including use of allograft tissue for orthopaedic surgeries.

    Device Description

    Force Fiber Fusion suture is an uncoated braid offered in a variety of cut lengths, with or without needles, and provided sterile for single use only. Force Fiber Fusion Suture is braided to transition from round suture to tape suture and back to round suture within the same braid construction. The tape section(s) are flat in shape and differ from USP requirements. The round section(s) exceed USP for diameter. The suture meets USP tensile strength requirements and USP needle attachment requirements. Force Fiber Fusion suture is available in sizes 1 through 5 and composed of undyed or blue Ultra High Molecular Weight Polyethylene (UHMWPE), UHMWPE/blue polyester co-braid, UHMWPE/black Nylon co-braid or UHMWPE/green Polyester co-braid.

    AI/ML Overview

    This document is a 510(k) Summary of Safety and Effectiveness for the Teleflex Force Fiber Fusion™ Suture. It describes the device, its intended use, and the testing performed to demonstrate substantial equivalence to predicate devices. However, this submission does not describe a study involving an AI/ML device or its acceptance criteria. Therefore, I cannot provide the requested information.

    The document discusses a surgical suture, which is a medical device, but it is not an AI/ML device. The submission is focused on demonstrating the substantial equivalence of a new suture design to existing, legally marketed predicate sutures through standard material and mechanical testing, not through performance metrics of an algorithm.

    Thus, key sections of the requested output, such as sample size for test sets, data provenance, number and qualifications of experts, adjudication methods, MRMC studies, standalone performance, type of ground truth, and training set information, are not applicable to the content provided.

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