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510(k) Data Aggregation

    K Number
    K093146
    Date Cleared
    2009-11-30

    (56 days)

    Product Code
    Regulation Number
    892.1750
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    IG4 IMAGE GUIDED SYSTEM, MODEL: SYS-0200

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

    The ig4™ Image Guided System is a stereotactic accessory for Computed Tomography (CT) or 3D fluoroscopic x-ray systems. The ig4 System is indicated for displaying an interventional instrument such as a biopsy needle, an aspiration needle, or ablation needle on a computer monitor that also displays a CT-based or 3D fluoroscopic x-ray-based model of the target organ(s). The ig4™ System compensates for the patient's respiratory phases.

    The ig4™ System is intended for use in clinical interventions and for anatomical structures where computed tomography or 3D fluoroscopic x-ray are currently used for visualizing such procedures.

    Device Description

    The ig4™ Image Guided System is an accessory for a CT or 3D fluoroscopic x-ray System that utilizes electromagnetic tracking technology to locate and navigate instruments relative to a CTbased or 3D fluoroscopic x-ray-based model of the patient anatomy. Due to system use to locate structures in soft tissue, the system incorporates a method of gating the location information on soft tissue to the patient's respiration. The ig4™ System consists of an EM tracking accessory for rigid needles or tip-tracked coaxial needle, a patient referencing system, an EM field generator and tracking system, software and a computer system.

    AI/ML Overview

    The provided text describes the Veran Medical Technologies ig4™ Image Guided System, an accessory for CT or 3D fluoroscopic x-ray systems. The submission is for an expansion of its indications for use to include navigation with 3D fluoroscopic x-ray-based models.

    Here's the breakdown of the acceptance criteria and study information:

    1. Table of Acceptance Criteria and Reported Device Performance:

    The document doesn't explicitly state quantitative acceptance criteria or a specific table with numerical values for performance. Instead, it relies on demonstrating substantial equivalence to a previously cleared device (ig4™ Image Guided System, K060903) for the expanded indication.

    Acceptance Criteria (Implied)Reported Device Performance
    Substantial equivalence to previously cleared device (K060903) for navigating interventional instrumentation in 3D fluoroscopic x-ray-based models of patient anatomy.Bench accuracy testing completed to demonstrate 3D fluoroscopic x-ray navigation accuracy on a static phantom.
    Safety and effectiveness demonstrated.All verification and validation activities completed by designated individuals, demonstrating safety and effectiveness.
    No required changes to the system and software of the predicate device for the new indication.This was affirmed: "There are no required changes to the system and the software of the ig4™ Image Guided System (K060903) for instrument navigation with 3D fluoroscopic x-ray-based models of the patient anatomy."

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

    • Test Set Sample Size: The document mentions "Bench accuracy testing... on a static phantom." It does not specify the number of tests or samples (e.g., number of measurements taken, different phantom configurations).
    • Data Provenance: The testing was "bench accuracy testing," implying a controlled laboratory environment. The country of origin of the data is not specified, but the applicant's address is in St. Louis, MO, USA. The study was prospective in nature, as it was conducted specifically to support this regulatory submission.

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

    Not applicable. The study involved bench accuracy testing on a static phantom, not a clinical study involving human patients or expert interpretation of diagnostic images. Therefore, the concept of "ground truth established by experts" as typically seen in image analysis studies does not apply here. The "ground truth" would be the known, precise physical dimensions and locations within the static phantom.

    4. Adjudication Method for the Test Set:

    Not applicable, as there was no expert review or human interpretation that would require adjudication. The bench accuracy testing would likely involve comparing the device's reported positions to the known, physical positions within the static phantom.

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

    No, an MRMC comparative effectiveness study was not done. The document explicitly states: "Clinical tests were not required to demonstrate the safety and effectiveness of the device."

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

    Yes, the "Bench accuracy testing" focused on the device's intrinsic 3D fluoroscopic x-ray navigation accuracy with a static phantom. This is a standalone performance evaluation, as it assesses the device's ability to accurately track and display without direct human intervention in the tracking mechanism itself, though a human would ultimately operate the system in a clinical setting.

    7. The Type of Ground Truth Used:

    For the "bench accuracy testing," the ground truth would be the known, precise physical dimensions and locations within the static phantom. This is a physical, measurable ground truth.

    8. The Sample Size for the Training Set:

    Not applicable. This device is a navigation system based on electromagnetic tracking and imaging, not a machine learning or AI-driven diagnostic algorithm that typically requires a separate training set. The system leverages existing algorithms and principles for localization and navigation, rather than being "trained" on a dataset in the way an AI model would be.

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

    Not applicable, as there was no training set in the context of machine learning or AI.

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