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510(k) Data Aggregation

    K Number
    K080550
    Device Name
    MEGACHANNEL ENDOSCOPIC OVERTUBE, MODEL OT22
    Manufacturer
    MINOS MEDICAL
    Date Cleared
    2008-06-10

    (103 days)

    Product Code
    ODB
    Regulation Number
    876.1500
    Type
    Traditional
    Panel
    Gastroenterology/Urology
    Reference & Predicate Devices
    K040836
    Why did this record match?
    Applicant Name (Manufacturer) :

    MINOS MEDICAL

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

    The Megachannel Endoscopic Overtube is indicated for use in conjunction with an endoscope for tissue or foreign body manipulations and/or where multiple removal and reinsertions of the endoscope are required.

    Device Description

    The Megachannel Endoscopic Overtube is a disposable flexible PVC tube that is to be used with an endoscope. The overtube includes a proximal handle with insufflation sealing cap that accommodates a standard 12.8 mm diameter colonoscope. A removable introducer plug is attached to the distal tip to facilitate the introduction of the overtube through the gastrointestinal tract.

    AI/ML Overview

    The provided text describes a medical device called the "Megachannel Endoscopic Overtube" and its 510(k) submission for market clearance. This document is a summary of the device and its claimed substantial equivalence to predicate devices, rather than a detailed study report on acceptance criteria and performance of an AI-powered device.

    Therefore, most of the information requested in your prompt (e.g., sample size for test set, number of experts, adjudication method, MRMC study, standalone performance, training set details) is not applicable or not available in this specific document, as it pertains to a traditional medical device (an overtube) and not an AI/ML-powered one. The submission focuses on functional and safety testing to demonstrate equivalence, not on AI model performance metrics.

    However, I can extract the information that is present according to your categories:

    1. Table of acceptance criteria and reported device performance

    The document does not explicitly state numerical "acceptance criteria" or "reported device performance" in the way one would for an AI algorithm (e.g., sensitivity, specificity, AUC). Instead, the performance demonstration is based on functional and safety testing to show equivalence to predicate devices.

    Acceptance Criteria (Implied)Reported Device Performance
    Safety and effectiveness equivalent to predicate devices for intended use"The Megachannel Endoscopic Overtube has been demonstrated to be as safe and effective as the predicate devices for its intended use."
    Successful functional testing"The Megachannel Endoscopic Overtube has successfully undergone functional testing. These products have been shown to be equivalent to the predicate devices."

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

    • Sample Size for Test Set: Not specified. The document states "functional testing" was performed, but does not detail the number of devices tested or the specific conditions/data used for this testing.
    • Data Provenance: Not applicable/not specified. This is a physical device, and "data provenance" (country of origin, retrospective/prospective) is typically associated with data used to train and test AI models.

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

    • Number of Experts: Not applicable/not specified. The device is an overtube, and "ground truth" as typically defined for AI algorithms is not relevant to its functional testing. Testing would involve engineering and performance assessments (e.g., material strength, dimensions, sealing capabilities).
    • Qualifications of Experts: Not applicable/not specified.

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

    • Adjudication Method: Not applicable/not specified. This concept is relevant to establishing ground truth for diagnostic AI, not for functional testing of a physical medical device like an overtube.

    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

    • MRMC Study: No. This document describes a physical medical device, not an AI-powered one that would assist human readers.
    • Effect Size: Not applicable.

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

    • Standalone Performance: Not applicable. This is a physical medical device, not an algorithm.

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

    • Type of Ground Truth: Not applicable in the context of AI. For this device, "performance" would be validated against engineering specifications, user requirements, and comparison to predicate device performance. This would likely involve measurements of physical properties, durability, and user interface elements, rather than diagnostic "ground truth" data.

    8. The sample size for the training set

    • Sample Size for Training Set: Not applicable. This is a physical device; there is no "training set" in the AI sense.

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

    • Ground Truth for Training Set: Not applicable. As there is no AI training set, this question is irrelevant to the provided document.
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