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510(k) Data Aggregation

    K Number
    K110160
    Device Name
    BEL-CYPHER N
    Manufacturer
    TAKARA BELMONT CORPORATION
    Date Cleared
    2011-02-17

    (29 days)

    Product Code
    MUH
    Regulation Number
    872.1800
    Type
    Special
    Panel
    Radiology
    Reference & Predicate Devices
    K090020
    Predicate For
    K163175
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Bel-Cypher N dental panoramic X-ray system is indicated for use as generator of radiographic images of the dento-maxilofacial region and is intended for dental examination and diagnosis of diseases of the tooth, jaw, and oral structures.

    Device Description

    The Bel-Cypher N dental panoramic X-ray system is indicated for use as a generator of radiographic images of the dento-maxilofacial region and is intended for dental examination and diagnosis of diseases of the teeth, jaw, and oral structures.

    AI/ML Overview

    The provided text is a 510(k) summary and FDA clearance letter for the Bel-Cypher N dental panoramic X-ray system. This document focuses on the device itself (an X-ray system) and its substantial equivalence to a predicate device, rather than on a specific algorithm or AI component that would have acceptance criteria and a study proving device performance against those criteria in the way you've outlined.

    The document discusses performance compliance in terms of general electrical and safety standards (IEC 60601 series), which are typical for medical devices like X-ray machines. It does not contain information about:

    1. A table of acceptance criteria and reported device performance for an AI algorithm.
    2. Sample size for a test set or data provenance related to AI performance.
    3. Number of experts for ground truth or their qualifications.
    4. Adjudication method.
    5. Multi-reader multi-case (MRMC) comparative effectiveness study with AI assistance. The device is the image generator.
    6. Standalone (algorithm-only) performance.
    7. Type of ground truth used for an AI algorithm.
    8. Sample size for a training set.
    9. How ground truth for the training set was established.

    Essentially, this S510(k) summary is for the hardware (the X-ray machine), not a software algorithm that would require the kind of performance validation you're asking about.

    Therefore, I cannot extract the requested information from the provided text as it does not pertain to the evaluation of an AI or algorithmic medical device component against specific performance criteria.

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