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510(k) Data Aggregation

    K Number
    K233347
    Manufacturer
    Date Cleared
    2023-12-04

    (66 days)

    Product Code
    Regulation Number
    878.4350
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    Verruca-Freeze**®** H

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

    The Verruca-Freeze® H cryosurgical system is indicated for use in the following skin lesions:

    • · Actinic Keratosis
    • · Genital Warts
    • Lentigo
    • Molluscum Contaqiosum
    • Seborrheic Keratosis
    • Skin Tags
    • · Verruca Plantaris
    • · Verruca Vulgaris • Verruca Plana
    • Verruca Plana
    Device Description

    The Verruca-Freeze® H cryosurgical system is used for treatment of benign skin lesions.
    The device consists of:

    • Canister filled with a liquid mixture of the compressed gases dimethyl ether, propane, and isobutane
    • . Polyurethane foam swabs
    • . Detailed instructions including illustrated descriptions
      The main component of the device is the pressurized aerosol canister containing cryogen spray. The canister is used with foam swabs, which are non-sterile, single-use applicators that are disposed after each use. The foam swabs allow the cryogen to be localized to a target lesion.
      The one- to two-minute procedure begins by spraying cryogen onto a foam swab, which is then placed directly onto the lesion and held for 15-40 seconds. Upon removal of the swab from the skin, the frozen tissue appears white and begins thawing. The typical thaw time is 20-40 seconds.
      This treatment methodology is simple and has been an accepted practice used by physicians for decades. It utilizes a standard cryogen composition profile to freeze lesions and destroy the underlying tissue through evaporative cooling. On the cellular level, both extracellular and intracellular ice formation are occurring.
    AI/ML Overview

    The provided document is a 510(k) summary for the Verruca-Freeze® H cryosurgical system. It primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study proving that the device meets specific acceptance criteria through a machine learning or AI algorithm's performance.

    Therefore, the document does not contain the information requested regarding acceptance criteria and a study proving a device meets these criteria in the context of an AI/ML powered device. The "performance data" mentioned refers to non-clinical tests of the cryosurgical process itself, not an algorithm's performance.

    Here's why the document cannot answer the prompt's specific questions about AI/ML device performance:

    • Device Type: The Verruca-Freeze® H is a cryosurgical system, a physical medical device that applies cold to treat skin lesions. It is not an AI-powered or software-based medical device.
    • "Study" Context: The "performance testing" described in Section 6.2 ("Non-Clinical Performance Data") is about measuring the output temperature and effectiveness of the cryogen and applicators, and visually confirming ice ball formation. This is physical hardware performance testing, not an algorithmic study.
    • Absence of AI/ML Specifics: There is no mention of algorithms, training sets, test sets, ground truth establishment by experts, adjudication methods, or MRMC studies, all of which are relevant to evaluating AI/ML-based medical devices.
    • "Clinical Performance Data": Section 6.3 explicitly states, "There was no clinical testing required to support the medical device as the indications for use are equivalent to those of the predicate device. These types of devices have been on the market for decades and their clinical safety and efficacy has been established." This further confirms that no new clinical study was conducted for this submission, let alone one focused on AI/ML performance.

    In summary, the provided document is a regulatory submission for a traditional medical device demonstrating substantial equivalence, not an AI/ML device showing performance against specific acceptance criteria.

    Therefore, I cannot populate the requested table or answer the specific questions about the study design (sample sizes, expert qualifications, adjudication, MRMC, standalone performance, ground truth types, training set details) because this information is not present in the provided text.

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