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510(k) Data Aggregation

    K Number
    K160346
    Device Name
    MX30N(MX30TQS)
    Manufacturer
    Date Cleared
    2016-04-14

    (66 days)

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

    The MX30N(MX30TQS) LCD Monitor System is intended to be used in displaying and viewing digital medical images for review and analysis by trained medical practitioners. The display is not intended for mammography.

    Device Description

    MX30N(MX30TQS) is a flat panel hi-resolution LCD monitor system for displaying digital medical images. The system consists of a state-of-the-art LCD monitor and a high-resolution graphic control board that connects to a PACS workstation for grayscale image display. The WIDE controller board is installed into the PACS workstation computer or other computer system to display PACS medical images.

    AI/ML Overview

    The document describes the K160346 submission for the MX30N(MX30TQS) medical monitor system. This device is a medical display and not an AI/algorithm system. Therefore, the provided information does not include details on a study proving a device meets acceptance criteria related to AI or complex algorithm performance (e.g., diagnostic accuracy, reader improvement with AI assistance).

    The acceptance criteria and "study" described are for the technical performance of a medical display, ensuring it meets standards comparable to a predicate device.

    Here's an analysis based on the provided text, focusing on the monitor's performance rather than AI:

    1. A table of acceptance criteria and the reported device performance

    The document provides a comparison table of the subject device (MX30N(MX30TQS)) with a predicate device (WIDE 3MP TFT LCD MONITOR). The "acceptance criteria" are implied by meeting or exceeding the performance of the predicate device and complying with specific international standards for medical electrical equipment.

    ParameterAcceptance Criteria (Implied by Predicate/Standards)Reported Device Performance (MX30N(MX30TQS))
    Intended UseSame as predicate: Display and view digital medical images for review and analysis by trained medical practitioners (not for mammography).Same as predicate.
    Technological CharacteristicsSame as predicate (flat panel hi-resolution LCD monitor, graphic control board for PACS workstation).Same as predicate.
    LCD Panel Size≥ 21.3"21.3"
    Resolution≥ 2048 x 15362048 x 1536
    Pixel Pitch≤ 0.21mm x 0.21mm0.21075mm x 0.21075mm
    Brightness≥ 800 cd/m²1700 cd/m²
    Contrast Ratio≥ 600:11400:1
    Input SignalDVI-DDVI-I, DisplayPort
    Power Supply100~240 VAC, 50/60Hz100~240 VAC, 50/60Hz
    Color/MonochromeMonochromeMonochrome
    Electrical SafetyCompliance with IEC 60601-1Complied
    EMCCompliance with IEC 60601-1-2Complied
    Resolution TestMet acceptance criteria specified in standardsMet acceptance criteria
    Luminance TestMet acceptance criteria specified in standardsMet acceptance criteria
    Noise TestMet acceptance criteria specified in standardsMet acceptance criteria

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

    This information is not applicable in the context of an "AI/algorithm" test set because the device is a medical monitor. The "test set" for this device would refer to the physical unit(s) of the MX30N(MX30TQS) monitor used for engineering and performance validation testing.

    • Sample Size: Not specified, but typically one or a few production units are thoroughly tested for conformity to standards.
    • Data Provenance: Not applicable in the sense of patient data. The tests are performed on the device itself against technical specifications.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

    This is not applicable. The "ground truth" for a medical display's performance relates to its physical and electronic characteristics (e.g., brightness, resolution, compliance with electrical safety standards), not diagnostic interpretations. These are measured by calibrated equipment and verified against engineering specifications and international standards, not by clinical experts establishing a ground truth for medical images.

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

    This is not applicable. Adjudication methods like '2+1' or '3+1' are used for consolidating expert opinions on controversial cases in diagnostic image interpretation. This is not relevant for testing a display's technical specifications.

    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

    This is not applicable. MRMC studies evaluate the diagnostic performance of human readers, sometimes with and without AI assistance, on a set of medical cases. The MX30N(MX30TQS) is a display monitor, not an AI algorithm, and therefore such a study was not performed or necessary for its clearance.

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

    This is not applicable. This question refers to the performance of an AI algorithm on its own. The MX30N(MX30TQS) is a hardware display device, not an algorithm.

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

    The "ground truth" for this device's performance is based on engineering specifications and international consensus standards (e.g., IEC 60601-1, IEC 60601-1-2) for medical electrical equipment and displays. Performance parameters like resolution, luminance, contrast, and noise are objectively measured by testing equipment and compared against these predefined numerical standards. No clinical ground truth (like pathology or outcomes) is involved in certifying a display.

    8. The sample size for the training set

    This is not applicable. Display monitors are hardware devices and do not have "training sets" in the context of machine learning or AI.

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

    This is not applicable for the same reason as point 8.

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