Search Filters

Search Results

Found 1 results

510(k) Data Aggregation

    K Number
    K141061
    Device Name
    REMOTEYE VIEWER
    Manufacturer
    Date Cleared
    2014-08-01

    (99 days)

    Product Code
    Regulation Number
    892.2050
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The RemotEye Viewer software product is intended to be used as a fully functional, web-based medical image viewer to download, review, interpret, manipulate, visualize and print medical multi-modality image data in DICOM format, also stored in remote locations with respect to the viewing site. When interpreted by a trained physician, the medical images displayed by RemotEye Viewer can be used as an element for diagnosis.

    Typical users of RemotEye Viewer are trained professionals, including but not limited to radiologists, physicians, nurses and technicians.

    Device Description

    The RemotEye Viewer software product is a feature-rich, diagnostic-level, web-based DICOM medical image viewer that allows downloading, reviewing, interpreting, manipulating, visualizing and printing medical multi-modality image data in DICOM format, from a client machine. The DICOM images may be physically remote with respect to the viewing client, but reachable through a network.

    Typical users of RemotEye Viewer are trained professionals, including but not limited to radiologists, physicians, nurses and technicians.

    AI/ML Overview

    The provided text describes a 510(k) premarket notification for the "RemotEye Viewer" device, which is a medical image viewer. However, it does not include detailed information about acceptance criteria or a specific study proving the device meets those criteria in the way typically expected for performance-based AI/CAD devices.

    The document primarily focuses on establishing "substantial equivalence" to a predicate device (eFilm Workstation) based on intended use, technological characteristics, and functionality. It does not present a performance study with defined metrics, sample sizes, expert ground truth, or comparative effectiveness.

    Therefore, many of the requested sections will state that the information is not provided in the given text.

    Here's a breakdown based on the provided text:

    1. Acceptance Criteria and Reported Device Performance

    The document does not explicitly state acceptance criteria or report specific performance metrics for the RemotEye Viewer in the context of diagnostic accuracy, sensitivity, specificity, etc. The "Testing" section broadly mentions that the device is "tested according the specifications that are documented in 013-139 and 009_Al Verification and Validation document." These internal documents are not provided.

    MetricAcceptance Criteria (Not provided in text)Reported Device Performance (Not provided in text)
    (e.g., Diagnostic Accuracy, Sensitivity, Specificity)N/AN/A
    (e.g., Image Display Quality)N/APerformed as intended for viewing DICOM images
    (e.g., Functionality)All features listed in "Technological Characteristics in common" function correctly.Implemented and functioning as described.

    The document's primary claim related to performance is that its "different technological characteristics" (web-based, multi-platform, no local storage) "don't constitute any new intended use and don't raise new questions of safety and effectiveness." This implies that its core viewing and manipulation functions are expected to perform similarly to the predicate device.

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

    The document does not describe a test set in the context of diagnostic performance evaluation. It mentions "Testing" as an integral part of their software development process, but no details regarding specific test datasets, their size, or provenance are included.

    • Sample Size for Test Set: Not provided.
    • Data Provenance (e.g., country of origin, retrospective/prospective): Not provided.

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

    Since no specific diagnostic performance test set is described, there's no mention of experts establishing ground truth. The device is a viewer for images that are "interpreted by a trained physician" to "be used as an element for diagnosis," implying human interpretation is central, not an automated diagnostic output requiring ground truth.

    • Number of Experts: Not applicable/Not provided.
    • Qualifications of Experts: Not applicable/Not provided.

    4. Adjudication Method for the Test Set

    Not applicable, as no diagnostic performance test set is described.

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

    No, the document does not describe an MRMC comparative effectiveness study where human readers improve with AI vs. without AI assistance. The device itself is a viewer, not an AI/CAD system providing interpretations.

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

    Not applicable. The RemotEye Viewer is a medical image viewing software intended for use by "trained professionals" to review and interpret images. It is not an algorithm designed for standalone diagnostic performance.

    7. The Type of Ground Truth Used

    Not applicable, as no diagnostic performance evaluation requiring ground truth is described for the device itself. The primary function is image display and manipulation, not automated diagnosis.

    8. The Sample Size for the Training Set

    Not applicable. The RemotEye Viewer is a software viewer, not a machine learning model that requires a training set in the conventional sense for diagnostic performance. Its development involves software engineering, testing, and validation, not training on medical images for algorithmic performance.

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

    Not applicable, as there is no mention of a training set or ground truth in the context of machine learning model development.

    Ask a Question

    Ask a specific question about this device

    Page 1 of 1