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510(k) Data Aggregation

    K Number
    K141164
    Device Name
    ARTEMIS LIGHT ENGINE
    Manufacturer
    QUEST MEDICAL IMAGING
    Date Cleared
    2015-03-16

    (315 days)

    Product Code
    NTN
    Regulation Number
    876.1500
    Type
    Traditional
    Panel
    Gastroenterology/Urology
    Reference & Predicate Devices
    K130819,K123956
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Artemis Light Engine is a primary LED light source when used in conjunction with laparoscopes or surgical cameras to illuminated surgical sites that allow observation or manipulation of body cavities, hollow organs, and canals.

    Device Description

    The Light Engine is a family of light engines that are designed for laboratory and clinical use in applications. The Light Engine provides a light source of which the intensity can be controlled via a PC. The output power of the Light Engine is calibrated as to provide a linear response over the full range of intensity and life-time. The Light Engine can be controlled by an external software device, or directly with the serial port provided by the USB interface (RS232-via-USB).

    AI/ML Overview

    This 510(k) premarket notification for the Artemis Light Engine primarily focuses on demonstrating substantial equivalence to predicate devices through technical specifications and adherence to voluntary standards, rather than extensive clinical performance studies with specific statistical acceptance criteria for a diagnostic algorithm.

    Therefore, many of the requested categories regarding acceptance criteria, study details, expert involvement, and ground truth establishment, which are typical for studies validating diagnostic AI, are not explicitly provided or are not applicable in the context of this submission. The device is an LED light source, not a diagnostic algorithm.

    Here's a breakdown of the information available and what is not applicable based on the provided document:

    1. Table of Acceptance Criteria and Reported Device Performance

    Given that this is an LED light source and not a diagnostic AI, the acceptance criteria are not in terms of diagnostic metrics like sensitivity, specificity, or AUC, but rather functional performance.

    Acceptance Criteria (Implied)Reported Device Performance (Implied from "Performance Testing")
    Light output intensity control and linearity"The output power of the Light Engine is calibrated as to provide a linear response over the full range of intensity and life-time."
    Functionality as intended (safety & effectiveness)"Design Test and Verification demonstrates that the device functions as it is intended and its performance does not raise any issues of safety and effectiveness."
    Software functionality"Software verification testing was successfully performed and completed."
    Electromagnetic Compatibility (EMC)Compliance with "IEC 60601-1-2 Edition 3: 2007-03 Medical Electrical Equipment Part1-2: General Requirements For Basic Safety And Essential Performance -Collateral Standard: Electromagnetic Compatibility - Requirements And Tests."
    Risk ManagementCompliance with "ISO 14971 Second edition 2007-03-01, Medical Devices - Application of Risk Management to Medical Devices."

    The "study that proves the device meets the acceptance criteria" is referred to as "Performance Testing", specifically "Bench Testing protocol attached." While the summary mentions this, the details of the protocol are not provided in the excerpt.

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

    Not Applicable. As a medical device (LED light source), the testing mentioned refers to bench testing of the device's physical and software performance, not a clinical test set with patient data.

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

    Not Applicable. Ground truth, in the context of diagnostic algorithms, involves expert labeling of patient data. This is an LED light source, so such a "test set" and "ground truth" are not relevant. The testing would have involved engineering and quality control personnel validating the device's technical specifications.

    4. Adjudication method for the test set

    Not Applicable. There is no "test set" of patient data requiring adjudication in this submission.

    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. The Artemis Light Engine is an LED light source for illuminating surgical sites, not an AI-powered diagnostic tool for human readers. Therefore, an MRMC study comparing human readers with or without AI assistance is irrelevant to this device.

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

    Not Applicable. The Artemis Light Engine is a hardware device (LED light source with software control), not a standalone diagnostic algorithm.

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

    Not Applicable. For a light source, "ground truth" would be related to objective physical measurements of light output, color temperature, intensity, control accuracy, and adherence to safety standards, verified through calibrated instruments and engineering specifications. It's not clinical "ground truth" as understood for diagnostic systems.

    8. The sample size for the training set

    Not Applicable. The device is not an AI algorithm that requires a "training set."

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

    Not Applicable. No training set exists for this device.

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