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510(k) Data Aggregation

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    Device Name :

    Endoscopic Video Image Processor (RP-IPD-V2000EF); Single-Use Video Flexible Cysto-Nephroscope (RP-U-C01F

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

    Endoscopic Video Image Processor

    This device is used in conjunction with the video endoscopes produced by our company to process the images collected by the video endoscopes and send them to the display, and provide power for the endoscope.

    Single-Use Video Flexible Cysto-Nephroscope

    This product shall be used in conjunction with the Endoscopic Video Image Processor and other peripheral equipment for observation, diagnosis, photography and treatment within the bladder, urethra, and kidney. The device is suitable for professional healthcare facility environments such as hospitals and clinics.

    This product shall not be used for other purposes.

    Device Description

    The proposed device, Cysto-Nephroscope System, which includes a Single-Use Video Flexible Cysto-Nephroscope and an Endoscopic Video Image Processor is intended for observation, diagnosis, photography and treatment within the bladder, urethra, and kidney.

    The Single-Use Video Flexible Cysto-Nephroscope is available in two models with the different position of "U" and "D" mark on the handle and different deflection, RP-U-C01F, RP-U-C01F5. The Endoscopic Video Image Processor has only one model, RP-IPD-V2000EF.

    The Single-Use Video Flexible Cysto-Nephroscope is sterile and single-patient-use device. The Endoscopic Video Image Processor is nonsterile and multi-patient-use device, and it will be cleaned and disinfected before first use and after each use.

    AI/ML Overview

    The provided text describes a 510(k) premarket notification for an Endoscopic Video Image Processor and Single-Use Video Flexible Cysto-Nephroscope. The document focuses on demonstrating substantial equivalence to a predicate device through performance testing. However, it does not contain specific acceptance criteria, numerical performance metrics for the device against these criteria, or a detailed study description with sample sizes, ground truth establishment, or expert involvement as requested.

    Therefore, I cannot provide the detailed table and study information you've requested from the provided text. The document states that the system "complies with the standard requirements" of ISO 8600 and IEC 62471:2006, and that optical and mechanical performance is "similar as those of the predicate device," but it does not quantify these compliance levels or similarities.

    Below is what can be extracted or inferred based on the provided text, highlighting the absence of the specific data you requested.


    Absence of Detailed Acceptance Criteria and Device Performance:

    The document broadly states that the device was evaluated for performance but does not provide a table specifying predefined acceptance criteria (e.g., minimum resolution, specific signal-to-noise ratio values) and the corresponding reported device performance values. It generally concludes that the device "complies with the standard requirements" (e.g., ISO 8600, IEC 62471:2006) and that its performance is "similar as those of the predicate device," but no quantitative data to support these statements is present.


    1. Table of Acceptance Criteria and Reported Device Performance

    Performance Metric CategorySpecific Metric (If Mentioned)Acceptance Criteria (As Implied/Stated)Reported Device Performance (As Stated)
    Optical PerformanceDirection of viewComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Depth of fieldComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    ResolutionComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Signal-to-noise ratioComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Geometric distortionComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Image intensity uniformityComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Dynamic rangeComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Color performanceComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Image qualityNot explicitly stated (compared to predicate)"similar" to predicate device
    Mechanical PerformanceBasic sizeComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Surface and edgesComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Water delivery systemComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Sealing performanceComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Compatibility with accessoriesComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Fatigue test (rocker & bending)Complies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Tensile strength (insertion)Complies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Cable connector performanceComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    Luer connector performanceComplies with ISO 8600 requirementsComplied with ISO 8600 requirements
    System PerformanceImage Frame FrequencyTestedResults not specified
    System DelayTestedResults not specified
    SafetyPhotobiological SafetyComplies with IEC 62471:2006Complied with IEC 62471:2006

    2. Sample size used for the test set and the data provenance

    The document does not specify the sample sizes used for any of the performance tests (e.g., number of instruments tested, number of images/frames evaluated). The data provenance is not explicitly stated beyond the company being Guangzhou Red Pine Medical Instrument Co., Ltd. from China. It is not specified if the tests involved retrospective or prospective data.

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

    The document does not mention the use of experts to establish ground truth for the test set, nor does it specify their number or qualifications. The performance evaluation seems to be based on instrumental measurements and comparisons to a predicate device, rather than expert interpretation of images.

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

    No adjudication method is mentioned, as there is no indication of expert review or consensus building for ground truth.

    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

    A multi-reader multi-case (MRMC) comparative effectiveness study was not mentioned. The document primarily focuses on technical performance characteristics of the device itself and comparison to a predicate device, not on human reader performance with or without AI assistance. The device described (Endoscopic Video Image Processor and Cysto-Nephroscope) is an imaging system, not an AI-driven diagnostic aid that assists human readers.

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

    This point is not applicable as the device is an imaging system and not an AI algorithm.

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

    The document does not describe the establishment of a "ground truth" in the diagnostic sense (e.g., expert consensus for disease presence or absence, pathological confirmation). Instead, the "ground truth" for these engineering and performance tests would largely be the established specifications within the relevant ISO and IEC standards, and the performance characteristics of the predicate device for comparative testing.

    8. The sample size for the training set

    This is not applicable, as the document describes an endoscopic imaging system, not an AI algorithm that requires a training set.

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

    This is not applicable, as there is no training set for an AI algorithm.

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