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510(k) Data Aggregation

    K Number
    K981543
    Date Cleared
    1998-06-08

    (39 days)

    Product Code
    Regulation Number
    874.4680
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    OLYMPUS LF-TP AND LF-DP TRACHEAL INTUBATION FIBERSCOPES, ACCESSORIES AND ANCILLARY EQUIPMENT

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

    Olympus LF-TP and LF-DP Tracheal Intubation Fiberscope, accessories and ancillary equipment are intended for airway management which includes endoscopic observation to assess airway anatomy, endotracheal/endobronchial intubation, and management.

    Device Description

    The Olympus LF-TP and LF-DP Tracheal Intubation Fiberscopes and it's accessories are specifically designed to be used with a Suction Pump and other ancillary equipment for airway management.

    AI/ML Overview

    This 510(k) summary for the Olympus LF-TP and LF-DP Tracheal Intubation Fiberscopes does not contain information on acceptance criteria for device performance, nor does it describe a study demonstrating the device meets such criteria in the way typically seen for AI/ML devices.

    Instead, this submission focuses on establishing substantial equivalence to previously marketed predicate devices (Olympus LF-1 Intubation Scope, K850978, and others) by demonstrating similar intended use, method of operation, materials, and design, and compliance with general safety standards.

    Therefore, the requested information cannot be fully provided from the given document as it pertains to a different type of device and regulatory submission.

    However, based on the provided text, here's what can be extracted and what cannot:

    1. A table of acceptance criteria and the reported device performance

    • Not available. The document does not specify performance acceptance criteria, nor does it provide quantitative performance metrics for the Olympus LF-TP and LF-DP fiberscopes. The submission relies on demonstrating substantial equivalence to predicate devices, implying similar performance without explicit numerical targets.

    2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

    • Not applicable/Not available. This type of information is relevant for studies evaluating the performance of AI/ML algorithms or human readers. For this device (an endoscope), performance is typically assessed through engineering tests, material compatibility, and functional verification, not through test sets of data in the same way. The document mentions "non-clinical tests" but does not detail their nature or sample sizes.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

    • Not applicable/Not available. Ground truth establishment by experts is not a relevant concept for the regulatory submission of an endoscope. Its functionality is assessed through physical and mechanical testing, and its safety through compliance with standards.

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

    • Not applicable/Not available. Adjudication methods are used in studies involving human interpretation of data, often in the context of AI evaluation. This is not relevant for the type of device described.

    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

    • Not applicable. This device is a medical instrument (fiberscope), not an AI system. Therefore, MRMC studies are not relevant.

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

    • Not applicable. This device is not an algorithm.

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

    • Not applicable/Not available. The concept of "ground truth" as used in AI/ML performance evaluation does not apply to this device. For an endoscope, "truth" relates to its physical specifications, image quality, and ability to perform its intended function safely.

    8. The sample size for the training set

    • Not applicable. This device is an endoscope, not an AI/ML algorithm that requires a training set.

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

    • Not applicable. As above, this device is not an AI/ML algorithm.

    Summary of what is present in the document regarding "studies":

    • Non-Clinical Tests: The document states that "Olympus LF-TP and LF-DP Tracheal Intubation Fiberscopes are designed, manufactured, and tested in compliance with the requirements of the voluntary safety standard, IEC 60601-1."
    • Conclusion from Non-Clinical Tests: The conclusion is that the new devices do not incorporate any significant changes in intended use, method of operation, material, and design that could affect device safety or effectiveness compared to predicate devices. This implies that the tests performed (likely engineering and safety compliance tests) confirmed that these aspects are equivalent.

    In essence, this 510(k) submission is for a conventional medical device (an endoscope) seeking clearance based on substantial equivalence to existing devices and compliance with safety standards, rather than demonstrating performance against specific clinical acceptance criteria via a dedicated human-in-the-loop or standalone AI study.

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