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510(k) Data Aggregation

    K Number
    K031436
    Device Name
    MEDISCOPE
    Date Cleared
    2003-12-19

    (227 days)

    Product Code
    Regulation Number
    884.1630
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    Gynecological and (Obstetrical colposcope MEDISCOPE, Diagnostic Device), is intended for magnified viewing of the tissues of cervix.

    Device Description

    MEDISCOPE, colposcope ( Obstetrical and Gynecological Diagnostic Device ) is a medical camera unit and accessories to be used to magnify and view tissues of the cervix and diagnose abnormalities and select areas for biopsy. Mediscope consists of a main unit ( power source ) and probe. MEDISCOPE is designed for effective communication and education between doctors and patients. MEDISCOPE can be connected to a TV, CCTV, or Color Video Printer.

    AI/ML Overview

    This document is a 510(k) premarket notification for a colposcope, which is a medical device. As such, the information provided focuses on demonstrating substantial equivalence to a predicate device and adherence to safety standards, rather than the kind of clinical study data typically associated with novel AI-driven diagnostic devices. Therefore, many of the requested fields regarding acceptance criteria and performance against a gold standard derived from clinical studies will not be found in this type of document.

    Here's an analysis based on the provided text:

    1. Table of Acceptance Criteria and the Reported Device Performance

    Acceptance CriteriaReported Device Performance
    Substantial Equivalence to Predicate Device"SOMETECH INC. BELIEVES THE MEDISCOPE IS SUBSTANTIALLY EQUIVALENT TO THE WELCH ALLYN COLPOSCOPE/VERTICAL ROLLING BASE/SWING ARM (510(K) NUMBER K955635)."
    Design Specifications"MEDISCOPE MEETS ALL DESIGN SPECS"
    Electrical Safety StandardsMet IEC 60601-1 (Medical electrical equipment Part 1: General requirements for safety.)
    Electromagnetic Compatibility (EMC) StandardsMet En 60601-1-2 (Standard for Electromagnetic Compatibility.)
    General Safety Requirements for Medical Devices"MEDISCOPE meets or exceeds all safety requirements for a medical device in its class."

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

    This document does not describe a clinical performance study with a "test set" in the context of diagnostic accuracy. The device is a colposcope, a visual magnification tool for examination. The evaluation focuses on safety and technical equivalence to a predicate device, not on diagnostic output.

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

    Not applicable. As noted above, this submission is for a medical device (colposcope) that is a tool for visual examination, not a diagnostic AI algorithm. Therefore, no "ground truth" establishment by experts in the context of a diagnostic performance study is described.

    4. Adjudication method for the test set

    Not applicable. No diagnostic performance test set is 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 is not an AI-driven device, and no MRMC study is mentioned.

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

    Not applicable. This is not an AI algorithm.

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

    Not applicable. The "ground truth" used for this submission is adherence to established medical device standards for design, safety, and equivalence to a legally marketed predicate device.

    8. The sample size for the training set

    Not applicable. No machine learning model or training set is described.

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

    Not applicable. No machine learning model or training set is described.

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