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510(k) Data Aggregation

    K Number
    K150130
    Manufacturer
    Date Cleared
    2015-05-28

    (127 days)

    Product Code
    Regulation Number
    888.3080
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    Ascendant TM Cervical Spacer System

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

    The Ascendant™ Cervical Spacer System is indicated for anterior cervical interbody fusion procedures in skeletally mature patients with degenerative disc disease at one disc level from C2-T1. Degenerative Disc Disease (DDD) is defined as discogenic pain with degeneration of the disc confirmed by history and radiographic studies. These patients should have had six weeks of non-operative treatment. The Ascendant™ Cervical Spacer System is to be used with autogenous bone graft and supplemental fixation (i.e., an anterior cervical plate), and is implanted via an open, anterior approach. Patients with previous non-fusion spinal surgery at involved level may be treated with the device.

    Device Description

    The Ascendant™ Cervical Spacer System is an anterior cervical interbody device consisting of a PEEK Optima® LT1 (polyetheretherkeytone) implant cage with CP titanium coating and tantalum radiographic markers. It is intended for use as an interbody fusion device and is offered in a variety of heights, footprints and lordotic angles to accommodate varying anatomical conditions. The device features an enclosed chamber intended to be filled with autogenous bone graft material.

    The Ascendant™ Cervical Spacer System is intended to be used with supplemental fixation (i.e., an anterior cervical plate).

    AI/ML Overview

    The provided text is a 510(k) premarket notification for a medical device called the "Ascendant™ Cervical Spacer System." This document is for a spinal implant, not an AI/ML powered medical device or a diagnostic device. Therefore, the information required to answer the prompt, such as acceptance criteria for performance metrics like sensitivity, specificity, or AUC, as well as details about training/test sets, ground truth establishment, expert adjudication, or MRMC studies, is not present in this document. The document describes mechanical performance testing for the device's physical properties.

    However, I can extract the information related to the device's mechanical performance and present it as acceptance criteria and reported performance, even though it doesn't align with the type of acceptance criteria typically expected for AI/ML devices.

    Here's the information derived from the document:

    1. Table of acceptance criteria and the reported device performance:

    Test ModeAcceptance CriteriaReported Device Performance
    Static Compressive ShearSufficient strengthSufficient strength
    Static ExpulsionSufficient strengthSufficient strength
    Dynamic Axial CompressionSufficient strengthSufficient strength
    Dynamic Compressive ShearSufficient strengthSufficient strength
    Dynamic TorsionSufficient strengthSufficient strength
    Percent PorositySufficient strengthSufficient strength
    Coating ThicknessSufficient strengthSufficient strength
    Static ShearSufficient strengthSufficient strength
    Static TensileSufficient strengthSufficient strength
    Shear FatigueSufficient strengthSufficient strength
    AbrasionSufficient strengthSufficient strength

    Note: The document broadly states "The results of this non-clinical testing show that the strength of the Cervical Spacer System, Ti Coated is sufficient for its intended use and is substantially equivalent to legally marketed predicate devices." Specific numerical acceptance values or reported outcomes are not provided in this summary, only the conclusion that the device's strength is sufficient.

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

    This information is not applicable to the mechanical testing described. Mechanical tests typically use a certain number of physical samples of the device, not a "test set" in the context of data or images. The document does not specify the number of device samples used for each test. Data provenance (country of origin, retrospective/prospective) is also not applicable for this type of mechanical testing.

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

    This information is not applicable. The "ground truth" for mechanical performance is determined by the physical properties of the material and design, measured against engineering standards (e.g., ASTM standards), not by human expert opinion.

    4. Adjudication method for the test set:

    This information is not applicable, as ground truth is based on engineering standards and physical measurements, not on human adjudication.

    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:

    This is not applicable. The device is a physical spinal implant; it is not an AI-powered diagnostic or assistive tool for human readers.

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

    This is not applicable as the device is a physical medical implant, not an algorithm.

    7. The type of ground truth used:

    The "ground truth" in this context is the established mechanical engineering standards (e.g., ASTM F2077, ASTM F1854-09, ASTM F1044-05, ASTM F1147-05, ASTM F1160-05, ASTM F1978-00) for intervertebral body fusion devices. The device's performance is measured against these standards to demonstrate its "sufficient strength" and substantial equivalence to predicate devices.

    8. The sample size for the training set:

    This is not applicable as the document describes mechanical testing of a physical device, not an AI/ML model.

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

    This is not applicable as the document describes mechanical testing of a physical device, not an AI/ML model.

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