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510(k) Data Aggregation

    K Number
    K173890
    Date Cleared
    2018-08-23

    (245 days)

    Product Code
    Regulation Number
    888.3560
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    GMK Sphere - Kinematic Alignment

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

    The Evolis®/GMK® knee prosthesis is designed for cemented use in total knee arthroplasty, if there is evidence of sufficient sound bone to seat and support the components.

    This knee replacement system is indicated in the following cases:

    • · Severely painful and/or disabled joint as a result of arthritis, traumatoid arthritis, theumatoid arthritis.
    • · Avascular necrosis of femoral condyle.
    • · Post traumatic loss of joint configuration.
    • · Primary implantation failure.

    GMK Sphere can be implanted using a kinematic alignment approach. When a kinematic alignment approach is utilized, this knee replacement system is indicated in the following cases:

    • · Severely painful and/or disabled joint as a result of arthritis, traumatoid arthritis or polyarthritis.
    • · Collagen disorders, and/or avascular necrosis of the femoral condyle.
    • · Moderate valgus, varus, or flexion deformities.

    Tibial wedges cemented are to be attached to the tibial baseplate with both the fixing cylinders and bone cement. The screwed tibial augments are for screwed fixation to the tibial baseplate.

    In case a semi-constrained liner is used, an extension stem must be implanted both on the femoral components.

    In case a GMK Revision tibial tray is used, an extension stem must be implanted.

    Device Description

    The purpose of this submission is to gain clearance for the GMK Sphere – Kinematic Alignment surgical technique and the technique specific instruments. The proposed kinematic alignment technique is an alternative alignment strategy to the traditional mechanical alignment used for total knee arthroplasty. The purpose of the kinematic alignment technique is to restore normal knee function by aligning the distal and posterior femoral joint lines of the tibial component to those of the normal or pre-arthritic state.

    The GMK Sphere - Kinematic Alignment surgical technique will be used with the following components of the GMK Sphere System cleared under K121416, K140826, and K162035:

    • o Tibial Insert Fixed Flex: Left and Right, Sizes 1 - 6, 10 - 20 mm (including intermediate sizes 11 mm and 12 mm), UHMWPE Type 1, Fixation Screw: Ti6Al4V;
    • Femoral Component Left and Right, Sizes 1 7, Co-Cr-Mo; ●
    • Femoral Component Left and Right, Sizes 1+ 6+ (intermediate sizes), Co-Cr-Mo; ●
    • Tibial Tray Fixed Cemented Left and Right, 4 intermediate sizes, Co-Cr-Mo; ●
    • o Tibial Insert Fixed Flex, Left and Right, Sizes 1 - 6, 10 mm - 20 mm, UHMWPE Type 1, Ti6Al4V:
    • o Tibial Insert Fixed Flex, Left and Right, Sizes 1 - 6, 11 mm and 13 mm, UHMWPE Type 1. Ti6Al4V: and
    • instrumentation. ●
    AI/ML Overview

    This document is a 510(k) premarket notification for the "GMK Sphere - Kinematic Alignment" knee replacement system. It primarily focuses on demonstrating substantial equivalence to previously cleared predicate devices, particularly concerning the introduction of a new kinematic alignment surgical technique and associated instruments.

    Based on the provided text, the device itself is a "Knee joint patellofemorotibial polymer/metal/polymer semi-constrained cemented prosthesis." The study described is a demonstration of substantial equivalence rather than a study proving the device meets clinical acceptance criteria through, for example, a clinical trial or a study assessing AI performance.

    Therefore, the information requested in the prompt, especially concerning AI performance metrics, expert roles, and multi-reader multi-case studies, is not applicable to the content of this 510(k) submission.

    However, I can extract information related to the engineering and mechanical performance testing of the device, which aligns with device acceptance criteria for a physical medical device.

    Here's an interpretation based on the provided document:

    Study Type: This is a 510(k) Premarket Notification, which aims to demonstrate substantial equivalence to legally marketed predicate devices, not specifically to prove AI performance or the effect of AI assistance on human readers. The "study" described is a series of non-clinical, benchtop and cadaveric performance tests to ensure the new surgical technique and instruments maintain the safety and effectiveness of the existing GMK Sphere system.

    1. Table of Acceptance Criteria and Reported Device Performance:

    The document mentions that "verification activities, as identified through risk analysis, were conducted to written protocols with pre-defined acceptance criteria." However, the specific numerical acceptance criteria and the detailed reported performance values are not provided in this summary document. Only the types of tests performed are listed.

    Acceptance Criterion (Type of Test)Reported Device Performance (Summary from document)
    Mechanical resistance of the femoral component under physiological static and dynamic loadsDemonstrated to support substantial equivalence. (Implied: met pre-defined acceptance criteria, but specific values not reported in this summary).
    Wear behaviorDemonstrated to support substantial equivalence. (Implied: met pre-defined acceptance criteria, but specific values not reported in this summary).
    Range of motion (ROM)Tested for both predicate and subject devices. (Implied: results were consistent with predicate, met pre-defined acceptance criteria, but specific values not reported in this summary).
    Mechanical resistance of the tibial tray under physiological static and dynamic loadsDemonstrated to support substantial equivalence. (Implied: met pre-defined acceptance criteria, but specific values not reported in this summary).
    Sterilization validationDemonstrated to support substantial equivalence. (Implied: met pre-defined acceptance criteria, but specific values not reported in this summary).
    Shelf lifeDemonstrated to support substantial equivalence. (Implied: met pre-defined acceptance criteria, but specific values not reported in this summary).
    PyrogenicityDemonstrated to support substantial equivalence. (Implied: met pre-defined acceptance criteria, but specific values not reported in this summary).
    Expected LoadsTested for subject devices. (Implied: met pre-defined acceptance criteria, but specific values not reported in this summary).
    Level of ConstraintsTested for subject devices. (Implied: met pre-defined acceptance criteria, but specific values not reported in this summary).
    Primary and Secondary FixationTested for subject devices. (Implied: met pre-defined acceptance criteria, but specific values not reported in this summary).
    Cadaveric workshop for validation of the surgical technique and the relative dedicated instruments setConducted. (Implied: validated the technique and instruments).

    2. Sample Size Used for the Test Set and Data Provenance:

    • Sample Size: The document does not specify the numerical sample sizes for the various mechanical and cadaveric tests (e.g., number of components tested, number of cadavers used). It refers to "verification activities... conducted to written protocols."
    • Data Provenance: The tests are described as non-clinical (benchtop) and cadaveric, meaning they are laboratory or ex-vivo studies. The document does not provide information about the country of origin of the data beyond implicitly being part of Medacta's internal R&D process (Medacta International SA is based in Switzerland). The data is prospective in the sense that these tests were conducted specifically for this submission.

    3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications:

    This concept, as it relates to expert review of medical images for ground truth, is not applicable to this type of device and submission. The "ground truth" for mechanical and cadaveric tests would be established by the physical and engineering properties of the materials and the biomechanical behavior observed, measured against engineering standards and specifications. There's no mention of interpreting visual data by human experts for this purpose.

    4. Adjudication Method for the Test Set:

    Not applicable in the context of expert consensus for imaging or clinical outcomes. Test results are likely adjudicated against pre-defined engineering specifications and standards, not through a human consensus process.

    5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

    No. This type of study (MRMC) is relevant for AI-powered diagnostic devices where human readers interpret medical images. This 510(k) is for a physical knee implant and surgical technique, not an AI product assisting in diagnosis.

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

    No. This concept applies to AI algorithms. The "device" here is a physical implant and surgical instruments.

    7. The Type of Ground Truth Used:

    The "ground truth" for these tests are:

    • Engineering Specifications/Standards: Performance measured against established industry standards and internal design specifications for mechanical properties (e.g., strength, wear, range of motion).
    • Biomechanical Principles: For cadaveric studies, the "ground truth" is the observed anatomical and biomechanical response of the tissues and device under simulated physiological conditions.

    8. The Sample Size for the Training Set:

    Not applicable. There is no "training set" in the context of an AI algorithm described here. The development of the device (implant and technique) is based on engineering design, material science, and prior surgical experience, not machine learning from a dataset.

    9. How the Ground Truth for the Training Set was Established:

    Not applicable. As there is no training set for an AI algorithm, this question is not relevant to the provided document.

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