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510(k) Data Aggregation

    K Number
    K122059
    Date Cleared
    2013-03-18

    (248 days)

    Product Code
    Regulation Number
    888.3040
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    ALL-SUTURE ANCHOR TWIST DRILL FOR ALL-SUTURE ANCHORS INLINE DRILL GUIDE FOR ALL-SUTURE ANCHORS

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

    The Smith & Nephew, Inc. SUTUREFIX Ultra Suture Anchor is intended for the secure fixation of soft tissue to bone for the following indications:

    Hip
    Hip capsule repair (Acetabular labrum repair/reconstruction)

    Shoulder
    Capsular stabilization (Bankart repair, Anterior shoulder instability, SLAP lesion repairs, Capsular shift or capsulolabral reconstructions), Acromioclavicular separation repairs, Deltoid repairs, Rotator cuff tear repairs, Biceps tenodesis

    Foot and Ankle
    Hallux valgus repairs, Medial or lateral instability repairs/reconstructions, Achilles tendon repairs/reconstructions, Midfoot reconstructions, Metatarsal ligament/tendon repair/reconstructions, Bunionectomy

    Elbow, Wrist, and Hand
    Biceps tendon reattachment, Ulnar or radial collateral ligament reconstructions, Lateral epicondylitis repair

    Knee
    Extra-capsular repairs (Medial collateral ligament, Lateral collateral ligament, Posterior oblique ligament), Patellar realignment and tendon repairs (Vastus medialis obliquous advancement), Illiotibial band tenodesis

    Device Description

    The Smith & Nephew SUTUREFIX Ultra Suture Anchor is intended to provide secure . fixation of soft tissue to bone. The device consists of a soft suture anchor with an attached non-absorbable suture(s) preassembled onto an insertion device. The SUTUREFIX Ultra Suture Anchors are available pre-loaded with either one #2 suture or with two #1 sutures. This device is provided sterile, for single use only.

    AI/ML Overview

    Here’s a breakdown of the acceptance criteria and study information for the SUTUREFIX Ultra Suture Anchor, based on the provided text:

    Important Note: The provided text is a 510(k) premarket notification summary. This type of submission generally focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed clinical efficacy studies against acceptance criteria like you'd find for novel therapies or AI-driven diagnostic tools. Therefore, much of the requested information (especially regarding AI, human readers, sample sizes for training/test sets, and expert details for ground truth) is not applicable or not present in this document. The document primarily discusses mechanical performance data.


    Acceptance Criteria and Reported Device Performance

    Acceptance Criteria (Implied)Reported Device Performance
    Insertion Strength MetMet performance specifications for insertion strength
    Pull-out Strength MetMet performance specifications for pull-out strength
    Substantial Equivalence to Predicate DevicesConsidered substantially equivalent to currently marketed predicate devices, raising no new issues of safety and efficacy.

    Explanation: The document states, "Performance data demonstrates that the SUTUREFIX Ultra Suture Anchor has met performance specifications for insertion strength and pull-out strength and therefore, is considered substantially equivalent to the currently marketed predicate devices." This implies that the 'acceptance criteria' were the successful demonstration of adequate insertion and pull-out strength, and the ultimate acceptance criterion was deemed 'substantially equivalent' to the predicate devices.


    Study Details

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

      • Sample Size: Not explicitly stated. The document refers to "Performance data" but does not quantify the number of devices or tests performed.
      • Data Provenance: Not specified, but generally, such biomechanical testing would be conducted in a laboratory setting (e.g., in-vitro or ex-vivo testing). No information on country of origin for the data is provided, nor whether it was retrospective or prospective in a clinical sense.
    2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

      • Not Applicable. For a mechanical device like a suture anchor, ground truth is typically established through direct physical measurement and engineering standards, not through "expert consensus" in the medical diagnostic sense. There are no "experts" establishing a diagnostic ground truth.
    3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

      • Not Applicable. Adjudication methods like 2+1 or 3+1 are used in studies where human interpretation of medical images or data is involved to establish a consensus ground truth. This is a biomechanical performance study, not an interpretative one.
    4. 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:

      • Not Applicable. This device is a surgical implant, not an AI-assisted diagnostic or imaging device. Therefore, no MRMC study, AI assistance, or human reader improvement data is relevant or provided.
    5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

      • Not Applicable. This is a physical medical device, not an algorithm or AI.
    6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

      • Biomechanical Performance Metrics: Ground truth for this device would be based on empirically measured physical properties, such as the force required for insertion and the force required to pull out the anchor from a test material (e.g., bone surrogate or cadaveric bone). This is determined by engineering and biomechanical testing standards.
    7. The sample size for the training set:

      • Not Applicable. This is not an AI/ML device that requires a "training set."
    8. How the ground truth for the training set was established:

      • Not Applicable. As above, no training set for an AI/ML model.
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