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510(k) Data Aggregation

    K Number
    K060970
    Manufacturer
    Date Cleared
    2006-07-05

    (89 days)

    Product Code
    Regulation Number
    888.3030
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    AXYALOOP NONABSORBABLE BONE ANCHOR

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

    The AxyaLoop™ Nonabsorbable Bone Anchor is indicated for securing suture to bone. This device is intended for use in the following applications:

    Shoulder: Rotator cuff, Bankart, and SLAP lesion repair, Biceps tenodesis, Acromio-Clavicular separation and Deltoid repair, Capsular shift and Capsulolabral reconstruction

    Foot/Ankle: Lateral and Medial stabilization, Achilles tendon and Metatarsal ligament repair, Hallux Valgus and Midfoot reconstruction

    Knee: Medial collateral and Lateral collateral ligament repair, Patellar tendon and Posterior oblique ligament repair, Iliotibial band tenodesis

    Hand/Wrist: Scapholunate ligament, Radial collateral ligament and Ulnar collateral ligament reconstruction

    Elbow: Biceps tendon reattachment, Tennis elbow repair, Ulnar and Radial collateral ligament reconstruction

    Pelvis: Bladder neck suspension for urinary incontinence due to urethral hypermobility or intrinsic sphincter deficiency in females

    Device Description

    The device described in this submission is designed with a corkscrew style thread and is available in 3.0mm, 5.0mm and 6.5mm diameters, for use in a range of soft tissue to bone attachment procedures. The Axya Bone Anchor is available as a system together with a thread tap and a placement tool (driver). These accessories are the same types of instruments included in procedure sets for currently marketed bone anchor systems. Axya Medical believes that the accessory instruments are Class I Manual Surgical Instruments and are exempt from the premarket Notification regulations. The Axya Nonabsorbable Bone Anchor is prethreaded with USP polypropylene monofilament, USP nydon, USP braided polyester and USP braided polyethylene suture materials.

    The Nonabsorbable Bone Anchor is designed for use in both standard open surgical procedures and in minimally invasive (arthroscopic) surgical procedures.

    AI/ML Overview

    The provided document is a 510(k) summary for a medical device, the AxyaLoop™ Nonabsorbable Bone Anchor. It discusses substantial equivalence to predicate devices based on in vitro testing. However, it does not describe studies typically associated with AI/ML devices or diagnostic performance metrics often found in acceptance criteria for such devices (e.g., sensitivity, specificity, AUC).

    Instead, the acceptance criteria relate to the mechanical performance of the bone anchor.

    Here's an analysis based on the provided text, addressing the points where information is available:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criteria (Performance Metric)Predicate Device Performance / BenchmarksAxyaLoop™ Nonabsorbable Bone Anchor Performance
    Anchor pull-out strengthComparable to predicate devicesComparable to predicate devices; predominately the same failure mode.

    Explanation: The document states: "The 510(k) Notice contains summaries of in vitro studies that were conducted to evaluate the anchor pull-out strength as specified in the FDA Guidance Document for Testing Bone Anchor Devices (dated April 20, 1996)." and "The data presented demonstrate that the anchor pull-out force of the Axya AxyaLoop™ Nonabsorbable Bone Anchor was comparable to that of the predicate devices of similar corkscrew geometry. The failure mode observed for the Axya anchor was predominately the same as that of the predicate anchor."

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

    • The document mentions "in vitro studies" but does not specify the sample size for these tests (e.g., number of anchors tested, number of simulated bone samples).
    • Data provenance: "in vitro" implies laboratory testing, not human patient data. There is no information on country of origin.

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

    • Not applicable. For mechanical performance tests like pull-out strength, "ground truth" is typically established by direct measurement using calibrated equipment, not expert consensus. The "FDA Guidance Document for Testing Bone Anchor Devices" likely outlines the methodology.

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

    • Not applicable. This concept typically applies to human expert review of diagnostic images or clinical cases, not mechanical testing.

    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. This is a mechanical device, not an AI/ML diagnostic or assistive device. MRMC studies are not relevant here.

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

    • Not applicable. This device is a mechanical bone anchor, not an algorithm.

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

    • For "anchor pull-out strength," the "ground truth" would be the measured force at which the anchor fails or pulls out from the simulated bone, as determined by mechanical testing according to established testing standards (likely referenced in the FDA Guidance Document).

    8. The sample size for the training set:

    • Not applicable. The device is a mechanical bone anchor, not an AI/ML algorithm that requires training data.

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

    • Not applicable. As above, no training set for an algorithm is involved.

    In summary:

    This document describes a medical device clearance based on substantial equivalence to existing predicate devices, primarily through in vitro mechanical testing to demonstrate comparable performance in terms of pull-out strength and failure mode. The evaluation framework is entirely different from what would be expected for an AI/ML-driven diagnostic or assistive device.

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