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510(k) Data Aggregation

    K Number
    K103290
    Manufacturer
    Date Cleared
    2011-09-19

    (315 days)

    Product Code
    Regulation Number
    872.1800
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    EDLEN IMAGING - GEMINI DUSB

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

    Edlen Imaging's Gemini DUSB sensor is a USB-driven digital X-ray sensor that acquires dental intra-oral X-ray images. The sensor will be operated by trained dental and related healthcare professionals to acquire dental intra-oral radiographs.

    The Gemini sensor can be used either in combination with special positioning devices, to facilitate alignment with the x-ray beam, or it may also be positioned manually.

    Device Description

    The Gemini sensor is an indirect light converting digital X-ray detector. Incident X-rays are converted to visible light by a scintillating device, (material) such as Csl (Cesium lodide), the light is coupled optically to a light detection imager which is based on CMOS technology.

    The design of the sensor allows for automatic detection of incident X-rays to generate a digital data which when coupled with a software program will display images used for dental intra-oral applications.

    The Gemini Digital x-ray sensor supports USB 2.0 Direct connectivity to personal computers and or laptops, hence the name Gemini DUSB.

    The Gemini sensors employ built-in dedicated electronics and sensor software driver, to allow connectivity to a variety of software packages. The Gemini sensors are coupled with the currently marketed software, Apteryx XrayVision, K983111.

    AI/ML Overview

    The provided text describes a 510(k) premarket notification for the Edlen Imaging Gemini DUSB Digital X-ray sensor, which demonstrates substantial equivalence to predicate devices rather than providing a detailed clinical study with acceptance criteria and specific performance metrics. Therefore, several requested categories cannot be directly extracted or are not applicable from the provided document.

    Here's a breakdown of what can be inferred and what is not available:

    1. Table of Acceptance Criteria and Reported Device Performance

    Not available. The submission focuses on establishing substantial equivalence to legally marketed predicate devices (Schick Technologies CDR and Suni Medical Imaging Suni Ray II) based on shared indications for use, materials, design, and operational/functional features. It states the device is "as safe, as effective, and performs as well as or better than the predicate devices," but does not provide specific acceptance criteria or quantitative performance results in the format requested.

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

    Not applicable. This was a 510(k) submission for substantial equivalence based on technical characteristics and safety standards, not a clinical study with a test set of patient data.

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

    Not applicable, as no clinical test set requiring ground truth establishment is described.

    4. Adjudication Method for the Test Set

    Not applicable, as no clinical test set requiring adjudication is described.

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

    Not applicable. The document does not describe an MRMC study comparing human readers with and without AI assistance. The Gemini DUSB is a digital X-ray sensor, not an AI or CAD system designed to assist human readers.

    6. Standalone Performance Study (Algorithm Only)

    Not applicable. The device is a digital X-ray sensor, which is a hardware component for image acquisition, not an algorithm with standalone performance.

    7. Type of Ground Truth Used

    Not applicable, as no clinical test set requiring ground truth is described.

    8. Sample Size for the Training Set

    Not applicable, as the device is a digital X-ray sensor, not an AI or machine learning algorithm that requires a training set in this context.

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

    Not applicable, as there is no training set for an AI/machine learning algorithm described.

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