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510(k) Data Aggregation

    K Number
    K170307
    Device Name
    SunCHECK
    Manufacturer
    SUN NUCLEAR CORPORATION
    Date Cleared
    2017-10-25

    (266 days)

    Product Code
    IYE
    Regulation Number
    892.5050
    Type
    Traditional
    Panel
    Radiology
    Reference & Predicate Devices
    K161946,K141800
    Why did this record match?
    Device Name :

    SunCHECK

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

    SunCHECK is a software platform intended to collect, detect, compare, calculate, analyze, display, and store radiotherapy quality assurance and dosimetry data.

    Device Description

    SunCHECK is a server-based Web application which is accessible from any networked PC. It is intended to provide radiation therapy professionals with a platform that integrates patient QA, machine QA and data management workflows. This platform consists of a single GUI and database that is intended to provide a centralized view of a radiation therapy department's QA efforts.

    AI/ML Overview

    The provided document is a 510(k) premarket notification for the SunCHECK medical device. It describes the device's intended use and compares its technological characteristics to predicate devices. However, the document does not contain specific information about acceptance criteria or a detailed study proving the device meets acceptance criteria.

    The only statement related to performance data is:

    "Model 1299028 SunCHECK has been tested using appropriate bench testing methods. Test results of the modified device have demonstrated that the device performs within its design specifications and equivalently to the predicate devices."

    This is a very general statement and does not provide the specific details requested in your prompt.

    Therefore, I cannot extract the following information from the provided text:

    1. A table of acceptance criteria and the reported device performance
    2. Sample sized used for the test set and the data provenance
    3. Number of experts used to establish the ground truth for the test set and the qualifications
    4. Adjudication method for the test set
    5. If a multi reader multi case (MRMC) comparative effectiveness study was done, an effect size of how much human readers improve with AI vs without AI assistance
    6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
    7. The type of ground truth used
    8. The sample size for the training set
    9. How the ground truth for the training set was established
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