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510(k) Data Aggregation

    K Number
    K101051
    Device Name
    AVS A-LAT PEEK SPACERS, MODELS 48750XXX, 48751XXX, 48752XXX, 48753XXX
    Manufacturer
    STRYKER SPINE
    Date Cleared
    2010-08-12

    (119 days)

    Product Code
    MAX
    Regulation Number
    888.3080
    Type
    Traditional
    Panel
    Orthopedic
    Reference & Predicate Devices
    K082014,K093242,K083661,K071795,K081611
    Predicate For
    K133126,K141443,K143163,K181030
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Stryker Spine AVS® A-LAT PEEK Spacers are intervertebral body fusion devices indicated for use with autogenous bone graft in patients with degenerative disc disease (DDD) at one level or two contiguous levels from L2 to S1.

    DDD is defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies. The DDD patients may also have up to Grade I spondylolisthesis at the involved level(s). These patients should be skeletally mature and have six months of nonoperative therapy.

    The AVS® A-LAT PEEK Spacers are intended to be used with supplemental fixation systems that have been cleared for use in the lumbosacral spine.

    Device Description

    The AVS® A-LAT Peek Spacers are intervertebral body fusion devices intended for use as an aid in spinal fixation. The AVS® A-LAT PEEK Spacers are rectangular shaped, hollow frame implants with lateral fenestrations, machined from medical grade PEEK OPTIMA LTI per ASTM F2026. The spacers incorporate five (5) Tantalum (per ASTM F560) marker pins to aid in radiographic visualization. The AVS® A-LAT PEEK Spacers are available in a variety of sizes and angles that allow the surgeon to best choose the size suited to the patient's anatomy and pathology.

    AI/ML Overview

    The provided text describes the 510(k) summary for the Stryker Spine AVS® A-LAT PEEK Spacers, which are intervertebral body fusion devices. This document focuses on demonstrating substantial equivalence to predicate devices based on design and mechanical testing, rather than a clinical study evaluating the performance of an AI-powered diagnostic device. Therefore, many of the requested categories related to AI device evaluation (e.g., sample size for test set, number of experts, MRMC studies, standalone performance) are not applicable to this submission.

    Here's an analysis based on the provided document:

    Acceptance Criteria and Device Performance

    The "acceptance criteria" for this device are implicitly tied to demonstrating "substantial equivalence" to existing predicate devices, particularly through meeting relevant ASTM standards for mechanical performance. The study shows the device meets these criteria by performing the specified mechanical tests and demonstrating comparable results to predicates.

    Acceptance Criteria (Implied)Reported Device Performance
    Demonstrated static compression strength (per ASTM F2077)Performed and demonstrated substantially equivalent performance to predicate devices.
    Demonstrated dynamic compression strength (per ASTM F2077)Performed and demonstrated substantially equivalent performance to predicate devices.
    Demonstrated static compression shear strength (per ASTM F2077)Performed and demonstrated substantially equivalent performance to predicate devices.
    Demonstrated dynamic compression shear strength (per ASTM F2077)Performed and demonstrated substantially equivalent performance to predicate devices.
    Demonstrated resistance to subsidence (per ASTM F2267)Performed and demonstrated substantially equivalent performance to predicate devices.
    Similar design features to predicate devicesLateral fenestrations, serrations, comparable dimensions, Tantalum marker pins included.
    Similar materials to predicate devicesMachined from medical grade PEEK OPTIMA LTI per ASTM F2026, Tantalum marker pins per ASTM F560.
    Intended Use as intervertebral body fusion deviceIntended for use with autogenous bone graft in DDD patients at L2-S1, with supplemental fixation.

    Study Details (as applicable)

    1. Sample size used for the test set and the data provenance: Not applicable. This was a mechanical engineering study, not a clinical study on human subjects or a dataset for an AI model. "Test set" here refers to the actual physical devices tested in a lab. The document does not specify the number of devices tested for each mechanical test, but it would typically be a statistically relevant sample size for engineering testing. The data provenance is from laboratory testing of the device prototypes.

    2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for mechanical testing is established by the physical properties and performance measured against engineering standards, not expert consensus.

    3. Adjudication method for the test set: Not applicable. Adjudication methods are relevant for human interpretation tasks, not for objective mechanical testing.

    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 is a medical device (spinal implant), not an AI diagnostic tool.

    5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is a medical device (spinal implant), not an AI algorithm.

    6. The type of ground truth used (expert consensus, pathology, outcomes data, etc): The "ground truth" for this submission is based on established engineering standards (ASTM F2077, ASTM F2267) and material specifications (ASTM F2026, ASTM F560) for mechanical performance and material composition. The comparison is made against the known performance and characteristics of legally marketed predicate devices.

    7. The sample size for the training set: Not applicable. This refers to an AI training set, which is not relevant for this medical device submission.

    8. How the ground truth for the training set was established: Not applicable, as there is no training set for an AI model.

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