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510(k) Data Aggregation

    K Number
    K243321
    Date Cleared
    2025-02-07

    (107 days)

    Product Code
    Regulation Number
    876.1500
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    Endoscopic Video Image Processor (RP-IPD-V1000F)

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

    The Endoscopic Video Image Processor is used in conjunction with the Single-Use Video Flexible Cysto-Nephroscope (Models: RP-U-C01F, RP-U-C01FS) to process the images collected by the video endoscope and send them to the display, and provide power for the endoscope.

    Device Description

    The Endoscopic Video Image Processor is a video processing system intended for use during endoscopic procedures. It receives and processes image signals from a compatible video endoscope and produces live video images during endoscopic procedures. Apart from the image processing functions, it also provides the power supply for the endoscope.

    The Endoscopic Video Image Processor is a reusable device. It does not require sterilization before use, as there is no direct/indirect patient contact. The device needs to be cleaned and disinfected before use. and the cleaning and disinfection method is outlined in the Instructions for Use.

    AI/ML Overview

    The provided text describes the Endoscopic Video Image Processor (RP-IPD-V1000F) as a video processing system for endoscopic procedures. It details its functions, such as processing image signals from compatible video endoscopes, producing live video images, and providing power to the endoscope. The document specifies that the device does not require sterilization as there is no direct/indirect patient contact but needs cleaning and disinfection before use.

    Here's an analysis of the provided information regarding acceptance criteria and the supporting study:

    1. Table of Acceptance Criteria and Reported Device Performance

    The document does not explicitly present a table of "acceptance criteria" with specific pass/fail thresholds alongside "reported device performance" in a quantitative manner as typically expected. Instead, it lists general performance characteristics that were tested and states that the "Performance Testing demonstrated that the subject device and the predicate device have similar performance, and the subject device is as safe and effective as the predicate device."

    Here's a reconstruction based on the available information:

    Acceptance Criteria (Implied)Reported Device Performance
    Direction of viewThe testing showed similar performance to the predicate device.
    Field of viewThe testing showed similar performance to the predicate device.
    Depth of fieldThe testing showed similar performance to the predicate device.
    ResolutionThe testing showed similar performance to the predicate device.
    Signal-to-noise ratioThe testing showed similar performance to the predicate device.
    Geometric distortionThe testing showed similar performance to the predicate device.
    Image intensity uniformityThe testing showed similar performance to the predicate device.
    Dynamic rangeThe testing showed similar performance to the predicate device.
    Color performanceThe testing showed similar performance to the predicate device.
    Image Frame FrequencyThe testing showed similar performance to the predicate device.
    System DelayThe testing showed similar performance to the predicate device.

    The standards referenced are:

    • ISO 8600-1:2015 Endoscopes - Medical endoscopes and endotherapy devices. General requirements
    • ISO 8600-3:2019 Endoscopes. Medical endoscopes and endotherapy devices. Part 3: Determination of field of view and direction of view of endoscopes with optics

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

    The document does not provide information on the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). It mentions "the Cysto-Nephroscope System" as the subject of testing.

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

    The document does not provide information regarding the number of experts used or their qualifications for establishing ground truth. The testing appears to be primarily technical performance testing against ISO standards rather than a clinical evaluation requiring expert interpretation of medical images.

    4. Adjudication Method for the Test Set

    The document does not specify any adjudication method. Given that the testing appears to be technical performance testing of the device's imaging capabilities, a traditional adjudication method for medical image interpretation would likely not be applicable.

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

    The document explicitly states "IX. Clinical Evidence N/A." This indicates that no human factors or comparative effectiveness study involving human readers with and without AI assistance was conducted or provided for this submission. The device is an image processor, not an AI-assisted diagnostic tool.

    6. Standalone Performance Study (Algorithm Only Without Human-in-the-Loop Performance)

    Yes, a standalone performance study was done in the form of "non-clinical performance testing." This testing evaluated the device's technical specifications and imaging capabilities (e.g., resolution, signal-to-noise ratio, color performance, image frame frequency, system delay) against relevant ISO standards. This is considered standalone performance as it assesses the device's intrinsic functional properties independent of human interaction or a clinical scenario.

    7. Type of Ground Truth Used

    The ground truth for the non-clinical performance testing was based on technical specifications and compliance with international standards (ISO 8600-1:2015 and ISO 8600-3:2019), rather than expert consensus on medical findings, pathology, or outcomes data, as this device primarily processes images.

    8. Sample Size for the Training Set

    The document does not mention a training set. This is expected as the device described is an "Endoscopic Video Image Processor" and is not presented as an AI/ML-driven diagnostic algorithm that would typically require a training set. It processes existing video signals rather than performing analysis for diagnostic insights.

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

    Since there is no mention of a training set, this information is not applicable.

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