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510(k) Data Aggregation

    K Number
    K031196
    Manufacturer
    Date Cleared
    2005-06-07

    (783 days)

    Product Code
    Regulation Number
    882.4560
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    SURGETICS ORTHO KNEELOGICS NAVIGATION SYSTEM

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

    The Surgetics ORTHO KNEELOGICS Navigation System is intended for use during stereotactic surgery to aid the surgeon in locating anatomical structures and aligning the endoprostheses with the anatomical structures. It is specifically indicated for assisting the surgeon during:

    • Anterior Cruciate Ligament (ACL) surgery by estimating anisometry of potential insertion points based on a digitized anatomical model
    • Total Knee Arthroplasty with ZIMMER NEXGEN implant
    • UniKnee Arthroplasty with BIOMET OXFORD Phase III implant
    Device Description

    The Surgetics ORTHO KNEELOGICS Navigation System (ORTHO KNEELOGICS) consists of the following major components and subsystems:

    • The Surgetics Station, consisting of a mobile computer system and an optical localizer
    • Ancillary instruments and reflective markers used for reference and registration
    • ORTHO KNEELOGICS Navigation applications software
    AI/ML Overview

    The provided text describes the Surgetics ORTHO KNEELOGICS Navigation System. Here's an analysis of the acceptance criteria and study information available:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance CriteriaReported Device Performance
    Electrical SafetyCompliance with electrical safety standards
    Electromagnetic CompatibilityCompliance with electromagnetic compatibility standards
    Accuracy (Phantom Testing)Summaries of accuracy testing provided
    Accuracy (Specimen Bench Testing)Summaries of specimen bench testing results provided
    Clinical StudySummaries of a clinical study provided
    Substantial Equivalence to Predicate Devices (BrainLab Vector Vision CT-Free (K021306) and OrthoSoft Navitrack System-Optical TKR CT-Less (K021760))"The ORTHO KNEELOGICS and the predicate systems all acquire positional data by tracking reflective markers and digitizing the relative locations of anatomical landmarks, and then use this data to display virtual 3D images used for subsequent planning and navigation during surgery. The three systems can all be used with different manufacturer's implant components."

    Critique: The provided document states that "summaries of accuracy testing using phantoms and specimen bench testing and results of a clinical study were provided," but it does not specify the numerical acceptance criteria (e.g., "accuracy within X mm") or detailed performance data (e.g., "achieved X mm accuracy"). Therefore, while the types of tests performed are mentioned, the actual quantitative criteria and the numerical results matching those criteria are not present in this excerpt. The substantial equivalence argument serves as a form of "acceptance criteria" related to technological characteristics.


    2. Sample Size for the Test Set and Data Provenance

    The document mentions "results of a clinical study were provided" but does not specify the sample size used for the test set (number of patients or cases) for the clinical study or the phantom/specimen testing.

    Data Provenance: Not explicitly stated. The submitter is from France ("Praxim 'Le Grand Sablon' 4, Avenue de l'Obiou 38 700 La Tronche France"), suggesting the possibility of data originating from France or Europe, but this is not confirmed. The document does not specify if the clinical data was retrospective or prospective.


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

    The document does not provide information on the number of experts used to establish ground truth for the test set or their specific qualifications.


    4. Adjudication Method for the Test Set

    The document does not specify any adjudication method (e.g., 2+1, 3+1, none) for establishing ground truth in the test set.


    5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

    The document does not mention or describe a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it discuss any effect size of how much human readers improve with AI vs. without AI assistance. The device is a navigation system used by a surgeon, not an AI diagnostic tool for human readers.


    6. Standalone Performance Study

    The accuracy testing on "phantoms and specimen bench testing" and the "clinical study" are likely standalone performance studies, as they aim to evaluate the device's accuracy and performance. However, the document does not explicitly label them as "standalone" studies or differentiate them from studies involving human interaction beyond the surgeon's use of the navigation system as intended.


    7. Type of Ground Truth Used

    The document mentions "accuracy testing using phantoms and specimen bench testing," which implies that the ground truth for these tests would be known true values or measurements derived from precise physical models (phantoms) or anatomical specimens. For the "clinical study," the type of ground truth is not explicitly stated but would typically involve intraoperative measurements, postoperative imaging, or clinical outcomes to assess the accuracy of the navigation system's guidance.


    8. Sample Size for the Training Set

    The document does not provide any information regarding a "training set" or its sample size. This device is described as an image-guided surgical navigation system that "acquires positional data by tracking reflective markers and digitizing the relative locations of anatomical landmarks," implying it's based on real-time positional data and anatomical models rather than a machine learning model that would require a distinct training set.


    9. How the Ground Truth for the Training Set Was Established

    Since no training set is mentioned or implied for a machine learning model, the document does not describe how ground truth for a training set was established.

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