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510(k) Data Aggregation

    K Number
    K973719
    Date Cleared
    1997-12-23

    (84 days)

    Product Code
    Regulation Number
    878.4810
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K950019

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

    For the removal or lightening of unwanted hair in the 1064nm mode only.

    Device Description

    Medlite™ Q-Switched Nd:YAG Laser
    Medlite™ IV Q-Switched Nd:YAG Laser

    AI/ML Overview

    The provided document is a 510(k) summary for a medical laser device (Medlite™ Q-Switched Nd:YAG Laser / Medlite™ IV Q-Switched Nd:YAG Laser) and does not contain information about an AI/ML powered device or software. Therefore, most of the requested information regarding acceptance criteria and a study proving an AI device meets those criteria cannot be extracted.

    The document indicates that the device's substantial equivalence was established by comparison to a predicate device (ThermoLase LT-100 Nd:YAG Laser Hair Removal System, K950019). The "Clinical Performance Data" section states "Authorization provided to access data in K950019." This implies that the clinical data from the predicate device was used to support the safety and effectiveness of the new device, rather than new clinical trials specifically for the Medlite laser.

    Below is an attempt to answer the questions based only on the provided text, highlighting what is missing due to the nature of the document.


    Acceptance Criteria and Device Performance for Continuum Biomedical's Medlite™ Q-Switched Nd:YAG Laser

    This document pertains to a traditional medical device (laser system) and therefore does not include detailed acceptance criteria or a study design typical for an AI/ML-powered device. The regulatory submission relies on substantial equivalence to a predicate device.

    1. Table of Acceptance Criteria and Reported Device Performance
    This information is not present in the provided 510(k) summary. For a traditional medical device like a laser, acceptance criteria would typically involve engineering specifications, safety standards compliance, and potentially clinical outcomes shown to be equivalent to a predicate device. The performance is assessed by comparison to the predicate.

    Acceptance Criteria (e.g., Performance Threshold)Reported Device Performance
    Not specified for this device type in the document.Not specified in this format.
    (Implied: Equivalent efficacy and safety for hair removal/lightening as the predicate device K950019)(Implied: Achieves removal or lightening of unwanted hair with comparable outcomes to K950019)

    2. Sample size used for the test set and the data provenance
    Not applicable/not provided for a standalone AI/ML device study. The clinical performance data relies on the predicate device (K950019). The document states, "Clinical Performance Data: Authorization provided to access data in K950019." This implies that the data from the predicate device's studies were referenced. The specific sample size, country of origin, or retrospective/prospective nature of that predicate data is not detailed here.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
    Not applicable/not provided. This concept (ground truth established by experts for a test set) is specific to AI/ML model evaluation. For this laser device, clinical efficacy against unwanted hair would likely have been assessed by clinicians or through patient outcomes in the predicate study.

    4. Adjudication method for the test set
    Not applicable/not provided. This refers to resolving disagreements among experts for ground truth establishment in AI/ML studies.

    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
    No, an MRMC comparative effectiveness study was not done, as this is not an AI-assisted device.

    6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
    Not applicable, as this is not an AI algorithm.

    7. The type of ground truth used
    Not applicable for an AI device. For the laser device's intended use, the "ground truth" for efficacy would likely be direct observation of hair removal/lightening by clinicians or patient self-reporting, as established in the predicate device's clinical data.

    8. The sample size for the training set
    Not applicable, as this is not an AI/ML device that requires a training set.

    9. How the ground truth for the training set was established
    Not applicable, as this is not an AI/ML device.

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    K Number
    K962446
    Date Cleared
    1997-03-07

    (256 days)

    Product Code
    Regulation Number
    878.4810
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K961279, K950019, K892514, K955612

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

    It is intended for use in dermatology for the removal of unwanted dark body hair.

    Device Description

    The Sharplan Ruby Laser System is a surgical laser which is capable of providing pulsed (O-Switched or Free Running) laser energy of a 694.3 nm wavelength through an articulated arm beam delivery system. While observing and directing the aiming beam (633 nm, HeNe), the treatment beam is administered by the physician through a foot switch.

    AI/ML Overview

    The Sharplan Lasers, Inc. Ruby Laser System is intended for use in dermatology for the removal of unwanted dark body hair. The submission does not specify quantitative acceptance criteria. Instead, it relies on demonstrating substantial equivalence to predicate devices and presenting clinical data to support safety and efficacy.

    Here's a breakdown of the available information:

    1. Table of Acceptance Criteria and Reported Device Performance:

    Acceptance Criteria (Explicit)Reported Device Performance
    Not explicitly stated as quantitative acceptance criteria in the provided text.Clinical data on 31 patients with a 3-month follow-up period demonstrating "safety and efficacy." The specific metrics for efficacy (e.g., percentage of hair reduction, duration of hair reduction) are not provided.

    2. Sample Size Used for the Test Set and Data Provenance:

    • Sample Size: 31 patients.
    • Data Provenance: The document does not specify the country of origin of the data. It is a prospective clinical study as it involved a 3-month follow-up period.

    3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts:

    • This information is not provided in the document. The study described is a clinical trial, but details about expert assessment of hair removal outcomes (e.g., by dermatologists or other specialists) and their qualifications are absent.

    4. Adjudication Method for the Test Set:

    • This information is not provided in the document.

    5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

    • No, an MRMC comparative effectiveness study was not done. This submission describes a clinical study to demonstrate the performance of the device itself, not a comparative study of human readers with and without AI assistance.

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

    • Not applicable. This device is a laser system, not an AI algorithm. Its performance is directly tied to its physical operation and effect on patients.

    7. The Type of Ground Truth Used:

    • The ground truth referred to implicitly by "safety and efficacy" would be clinical outcomes (e.g., observed hair removal, adverse events) as assessed during the 3-month follow-up period. However, the specific metrics and how they were measured are not detailed.

    8. The Sample Size for the Training Set:

    • Not applicable. This is a hardware device, not a machine learning model. Therefore, there is no "training set" in the context of AI.

    9. How the Ground Truth for the Training Set was Established:

    • Not applicable. See point 8.
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