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510(k) Data Aggregation

    K Number
    K192481
    Date Cleared
    2020-07-28

    (322 days)

    Product Code
    Regulation Number
    888.3060
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The AccelFix Lumbar Plate System is indicated for use via the anterolateral surgical approach above the bifurcation of the great vessels or via the anterior surgical approach, below the bifurcation of the great vessels as an adjunct to fusion. This system is indicated in the treatment of lumbar and lumbosacral (L1-S1) spine instability as a result of fracture (including dislocation and subluxation), tumor, degenerative disc disease (defined as back pain of discogenic origin with degeneration of the disc confirmed by patient history and radiographic studies), pseudoarthrosis, spondylolysis, scoliosis, spondylolisthesis, lordotic deformities of the spinal stenosis, or a failed previous spine surgery.

    Device Description

    The AccelFix Lumbar Plate System's implants are Lumbar Plate System intended for use as an aid in spinal fixation. They are made of Titanium 6AL-4V Alloy (ASTM F136). The AccelFix Lumbar Plate System consists of a variety of shapes and sizes of plates and screws. The plate has been designed to include spikes for added stability and alignment during screw insertion. The plates feature a curvature for anatomic fit. The diameter of screw is available from 5.5 to 6.0 mm and the length from 20 to 55 mm.

    AI/ML Overview

    The provided text is a 510(k) summary for a medical device called the "AccelFix Lumbar Plate System." It describes the device, its intended use, and its substantial equivalence to predicate devices, supported by bench testing.

    However, the information requested in your prompt (especially related to acceptance criteria and studies for AI/algorithm performance, multi-reader multi-case studies, ground truth establishment, training set details, etc.) is not present in the provided text. This document is for a spinal fixation orthosis, a physical medical device, not a software algorithm or AI-based diagnostic tool.

    Therefore, I cannot fulfill your request for the specific details within the provided text. The document does not discuss:

    • Acceptance criteria for an algorithm's performance
    • Device performance metrics like sensitivity, specificity, or AUC
    • Sample sizes for test sets in an AI study
    • Data provenance
    • Number/qualifications of experts for ground truth
    • Adjudication methods
    • MRMC studies or effect sizes for human readers with AI assistance
    • Standalone algorithm performance
    • Type of ground truth (e.g., pathology, outcomes data) for an algorithm
    • Training set size or how ground truth was established for a training set

    The document primarily focuses on mechanical bench testing for a physical implant. It states:

    • Acceptance Criteria Mentioned: "The AccelFix Lumbar Plate System met all pre-defined acceptance criteria and, in tests where it was compared to either AccelFix Lumbar Plate System the predicate or reference device, was found to not represent a new worst case." (Page 4, Section 3). The specific numerical values or thresholds for these acceptance criteria are not detailed.
    • Study Proving Acceptance: The study performed was "Bench testing to evaluate the mechanical properties of the AccelFix Lumbar Plate System" (Page 5, Section 7), following ASTM F 1717-18.
    • Reported Device Performance: "Bench testing to evaluate the mechanical properties of the AccelFix Lumbar Plate System showed a higher or similar mechanical value than predicate marketed devices." (Page 5, Section 7). Specific numerical performance metrics are not provided.
    • Types of Tests:
      • Static Compression Bending Test ASTM F1717-18
      • Static Tension Bending Test ASTM F1717-18
      • Static Torsional Test - ASTM F1717-18
      • Dynamic Compression Bending Test - ASTM F1717-18

    In summary, the provided document does not contain the information needed to answer your questions about AI device performance and corresponding studies.

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