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510(k) Data Aggregation

    K Number
    K231260
    Date Cleared
    2024-03-05

    (309 days)

    Product Code
    Regulation Number
    884.1690
    Reference & Predicate Devices
    Why did this record match?
    Applicant Name (Manufacturer) :

    Suzhou AcuVu Medical Technology Co., Ltd.

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

    HTx Disposable Hysteroscope System is intended to be used for viewing of the cervical canal and uterine cavity for the purpose of performing diagnostic and operative procedures.
    Note: Hysteroscopes are used as tools for access to the uterine cavity and are not, in and of themselves a method of surgery.

    Device Description

    The HTx Disposable Hysteroscope System is a single use hysteroscope endoscope intended for gynecology procedure applications. It includes a single-use, disposable hysteroscope cannula (HTx40 or HTx60), a reusable imaging system (HTx1000/Htx2000), and optional accessories (medical keyboard, barcode reader and IV pole mounting rack). The disposable hysteroscope contains a miniature CMOS camera, a light-emitting diode (LED) illumination module, and channels for fluid in/out flow as well as for instrument insertion. HTx60 has a cannula outer diameter of 6.2 mm and adapts to tools up to 3 mm (9 Fr), while HTx40 has an outer diameter of 4.5 mm and adapts to tools up to 5 Fr. The cannula connects directly to the image processor via an image cable. The image processor processes the raw image signal from the cannula and outputs the video on a display for real-time visualization. The image processor includes input/output ports for interfacing with different peripherals such as a keyboard or a USB flash disk. Fluid irrigation is achieved through a tube which is connected to an IV bag and pressured via peristaltic pump (not included in the subject device). The fluid inflow channel shares the same with the working channel.

    AI/ML Overview

    The provided text is a 510(k) Premarket Notification for a general medical device (HTx Disposable Hysteroscope System), not an AI/ML-driven device. Therefore, the document does not contain the specific information required to describe acceptance criteria and associated studies for AI/ML performance, such as:

    • A table of acceptance criteria and reported device performance for AI metrics (e.g., sensitivity, specificity, AUC). The document focuses on general device performance characteristics.
    • Sample size used for the test set and data provenance.
    • Number of experts and their qualifications for establishing ground truth.
    • Adjudication method for ground truth.
    • MRMC comparative effectiveness study, effect size.
    • Standalone (algorithm only) performance.
    • Type of ground truth (expert consensus, pathology, outcomes data).
    • Sample size for the training set.
    • How ground truth for the training set was established.

    The document primarily addresses the substantial equivalence of a new version of a hysteroscope system (HTx2000 image processor) to a previously cleared predicate device (HTx1000). The "non-clinical performance testing" section on page 12 refers to:

    • Bench-top tests: Color performance (spectrophotometer measurements, working distance, color contrast enhancement) for the HTx2000.
    • Basic electrical safety: IEC 60601-1:2005+AMD1:2012.
    • Electromagnetic compatibility (EMC): IEC 60601-1-2:2020.
    • Software testing: Validated as "Basic Documentation Level" according to FDA guidance (which is about software documentation for any medical device with software, not specific to AI/ML performance).
    • Battery safety: IEC 62133-2:2017/AMD1:2021 and UN38.3.

    These tests are standard for general medical device clearance and do not involve the specific types of performance studies or acceptance criteria associated with AI/ML functionality (e.g., diagnostic accuracy, clinical classification, or segmentation performance).

    In conclusion, based on the provided text, it is not possible to describe the acceptance criteria and study proving device performance in the context of an AI/ML device, as the submission does not pertain to such a device.

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