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510(k) Data Aggregation

    K Number
    K153401
    Device Name
    GR40CW
    Date Cleared
    2015-12-21

    (27 days)

    Product Code
    Regulation Number
    892.1680
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K152094, K102587

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

    The GR40CW Digital X-ray Imaging System is intended for use in general projection radiographic applications wherever conventional screen-film systems or CR systems may be used. This device is not intended for mammographic applications.

    Device Description

    The GR40CW digital X-ray imaging system consists of Detector, Power supply box, Battery pack, Battery charger, Access point, CIB(Control Interface Box), Workstation, Barcode scanner and Main cable. This system is used to capture images by transmitting X-ray to a patient's body. The X-ray passing through a patient's body is sent to the detector and then converted into electrical signals. These signals go through the process of amplification and digital data conversion in the signal process device being sent to the S-Station (Operation Software) and saved in DICOM file, a standard for medical imaging. The captured images are sent to the Picture Archiving & Communication System (PACS) server, and can be used for reading images.

    AI/ML Overview

    This document describes the premarket notification for the GR40CW Digital X-ray Imaging System (retrofit kit). The submission focuses on demonstrating substantial equivalence to predicate devices, particularly concerning the performance of a new detector, S4335-WV.

    Here's a breakdown of the requested information based on the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance

    The document does not explicitly present a table of acceptance criteria with corresponding performance metrics in a pass/fail format. Instead, it relies on demonstrating equivalence or improvement compared to predicate devices for key technical specifications and imaging performance metrics.

    Performance Metric / CharacteristicPredicate Device 1 (GR40CW, K152094) / Predicate Device 2 (LLX240AB01, K102587)Proposed Device (GR40CW with S4335-WV)Discussion / Equivalence Claim
    Detector TypePredicate 1: CsI / Predicate 2: Gd2O2SGd2O2SSame as 2nd predicate device
    Detector AreaPredicate 1: 14"X17" (345mmX425mm) / Predicate 2: 17"X17" (439mmX439mm)14"X17" (345mmX425mm)Same as 1st predicate device
    Pixel Pitch (µm)Predicate 1: 140 / Predicate 2: 143140Same as 1st predicate device
    High Contrast Limiting Resolution (lp/mm)Predicate 1: 3.5 / Predicate 2: 3.63.5Same as 1st predicate device
    MTF (Modulation Transfer Function)Mentioned for Predicate 2Curves and measurements providedDemonstrates substantially equivalent or improvement to the 2nd predicate device
    DQE (Detective Quantum Efficiency)Mentioned for Predicate 2Curves and measurements providedDemonstrates substantially equivalent or improvement to the 2nd predicate device
    Safety (Electrical, Mechanical, Environmental)Compliant with ES 60601-1(2012)Compliant with ES 60601-1(2012)Satisfying with the standards
    EMC (Electromagnetic Compatibility)Compliant with IEC 60601-1-2(2007)Compliant with IEC 60601-1-2(2007)Satisfying with the standards
    Wireless FunctionTested and verifiedTested and verifiedFollowed guidance for Radio frequency Wireless Technology in Medical Devices

    2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

    The document mentions "clinical images were obtained in accordance with FDA guidance for the submission of 510(k)'s for Solid State X-Ray Imaging Devices." However, it does not specify the sample size for the test set or the data provenance (country of origin, retrospective or prospective).

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

    The clinical images were "evaluated by a professional radiologist." The document mentions "a professional radiologist," implying a single expert was used. Their specific qualifications (e.g., years of experience, subspecialty) are not provided.

    4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

    The document states the images were "evaluated by a professional radiologist." This suggests no formal adjudication method (like 2+1 or 3+1) was employed, as only one radiologist is mentioned as evaluating the images.

    5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

    No MRMC comparative effectiveness study was done. The device GR40CW is described as a "Digital X-ray Imaging System" and a "retrofit kit" that generates digital images. It is not an AI-powered diagnostic tool, but rather a digital detector replacing analog film systems. Therefore, the question of human reader improvement with AI assistance is not applicable to this device.

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

    Not Applicable. The GR40CW is a digital X-ray imaging system, not an algorithm, so a standalone performance evaluation of an "algorithm only" is not relevant. Its performance is tied to its image acquisition capabilities, which are then interpreted by human readers.

    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

    The ground truth for the clinical images was established by a professional radiologist's evaluation. It is not explicitly stated whether this involved objective measures like pathology or outcomes data, but rather the radiologist's interpretation of the images.

    8. The sample size for the training set

    The document does not specify any training set size. The context of the submission is a 510(k) for a device with a new detector, focusing on demonstrating equivalence to existing technology, not on training an AI algorithm.

    9. How the ground truth for the training set was established

    Not Applicable. As no training set is mentioned in the context of an AI algorithm, the method for establishing ground truth for a training set is not discussed.

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