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510(k) Data Aggregation

    K Number
    K172004
    Device Name
    truSculpt
    Manufacturer
    Date Cleared
    2017-08-02

    (30 days)

    Product Code
    Regulation Number
    878.4400
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K160470

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

    The truSculpt RF energy is intended to provide topical heating for the purpose of elevating tissue temperature for the treatment of selected medical conditions, such as relief of pain and muscle spasms and increase in local circulation.

    Additionally, the 2 MHz setting for the 40 cm² handpiece is indicated for temporary reduction in circumference of the abdomen.

    The truSculpt massage device is intended to provide a temporary reduction in the appearance of cellulite.

    Device Description

    The modified truSculpt RF device consists of a console; one 16 cm² RF handpiece; up to six 40 cm² puck-style RF handpieces that can attach to belts configured for hands-free abdominal and flank treatments for circumferential reduction; adjustable patient belts; a patient comfort switch; and a truGlide massage roller. All system functions are controlled through the console. The handpieces deliver RF energy to generate a heating profile that produces a moderate temperature rise in the subcutaneous tissue, while monitoring epidermal temperature. In addition, there is a separate mechanical roller that can be used as a massager.

    AI/ML Overview

    The provided document K172004 is a 510(k) premarket notification for a medical device called "truSculpt". It primarily focuses on demonstrating substantial equivalence to predicate devices and describes modifications to the existing truSculpt RF device. The document does not contain information about acceptance criteria or a study proving the device meets acceptance criteria in the context of an AI-powered diagnostic device, which is what the prompt is implicitly asking for (e.g., using terms like "human readers improve with AI vs without AI assistance", "standalone (i.e. algorithm only without human-in-the-loop performance)", "ground truth").

    The truSculpt device is an electrosurgical cutting and coagulation device, specifically an RF energy device for topical heating, temporary reduction in circumference of the abdomen, and temporary reduction in the appearance of cellulite, and a massage device. This is a physical device used for treatment, not an AI-powered diagnostic tool.

    Therefore, I cannot provide the requested information from this document. The document describes:

    1. Device: truSculpt (RF energy device and massage device).
    2. Modifications: Addition of a multiplexor board to power up to six treatment handpieces sequentially, a patient-activated treatment termination switch, and belts for hands-free operation of 40 cm² handpieces for circumferential reduction.
    3. Safety and Performance Data: Reference to compliance with IEC 60601-1 standards (general safety, usability, electromagnetic disturbances, high-frequency surgical equipment) and AAMI/ANSI ES60601-1. It also mentions software verification and validation (V&V) for the patient comfort switch and multiplexor board.
    4. Clinical Trial Mention: States that "the identical treatment parameters are available for selection as used in the clinical trial to gain this indication for use, including area treated and RF dose (time and temperature)" for abdominal circumferential reduction treatments. However, it does not provide details of this clinical trial or specific performance metrics from it in a way that relates to the prompt's request for AI acceptance criteria.

    Based on the provided document, I cannot fill out the requested table or answer the specific questions related to AI device performance and ground truth establishment. The document is about a physical device's electrical safety and functional equivalence, not an AI model's diagnostic performance.

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    K Number
    K171111
    Device Name
    Sculpsure
    Manufacturer
    Date Cleared
    2017-06-13

    (60 days)

    Product Code
    Regulation Number
    878.5400
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    Cynosure SculpSure K160470, Zeltiq Coolsculpting K162050

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

    The Cynosure SculpSure™ is intended for non-invasive lipolysis of the abdomen, flanks, back, and thighs in individuals with a Body Mass Index (BMI) of 30 or less. The device is intended to affect the appearance of visible fat bulges in the abdomen, flanks, back, and thighs.

    Device Description

    The Cynosure SculpSure is a diode laser system. The main components of SculpSure are a console and four applicators that deliver the laser energy to the patient. Electrically efficient semiconductors generate optical radiation (1060 nm) which is used to deliver laser energy to subcutaneous tissue layers.

    AI/ML Overview

    Acceptance Criteria and Study Details for SculpSure Device

    The SculpSure device is intended for non-invasive lipolysis to reduce fat in specific body areas. The following information details the acceptance criteria and the study that demonstrated the device meets these criteria.

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance CriteriaReported Device Performance
    Effectiveness - Photographic AssessmentBlind evaluators able to identify 86% of post-treatment photographs (Back: 91%, Outer Thigh: 87%, Inner Thigh: 83%).
    Effectiveness - Fat Reduction (Ultrasound)At 12-week post-follow-up, average 8.6% normalized fat reduction (Back: 10.6%, Outer Thigh: 7.2%, Inner Thigh: 8.0%). All treatments had a *p-value of X% fat reduction"). Instead, it reports the observed performance and concludes that the results demonstrate safety and effectiveness for substantial equivalence. The reported performance values in the table are the actual results from the study.)

    2. Sample Size and Data Provenance for Test Set

    • Sample Size: A total of 168 subjects, making up 214 treatment areas, were enrolled in the study.
      • Back: 55 subjects / 55 treatment areas
      • Outer Thigh: 52 subjects / 52 treatment areas
      • Inner Thigh: 61 subjects / 107 treatment areas
    • Data Provenance: The study was a prospective, controlled study conducted at 3 study centers. The specific country of origin is not explicitly stated, but it can be inferred to be within the US given the submission to the FDA.

    3. Number of Experts and Qualifications for Ground Truth

    The document mentions "blind evaluation of pre and post treatment (12-week) photographs," and "blind evaluators were able to identify 86% of the post treatment photographs." However, the number of experts used to establish the ground truth for the test set and their specific qualifications are not provided in the provided text.

    4. Adjudication Method

    The document states "blind evaluation of pre and post treatment (12-week) photographs." This indicates a degree of blinding. However, the specific adjudication method (e.g., 2+1, 3+1, none) is not explicitly stated for establishing ground truth or for resolving discrepancies in photographic evaluation.

    5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

    No MRMC comparative effectiveness study was done comparing human readers with and without AI assistance. The study evaluated the device's efficacy in non-invasive fat reduction.

    6. Standalone (Algorithm Only) Performance

    This device is a laser system (SculpSure), not an AI algorithm. Therefore, the concept of "standalone (i.e., algorithm only without human-in-the-loop performance)" does not apply to this medical device. The study evaluates the performance of the physical device as used by a human operator.

    7. Type of Ground Truth Used

    The efficacy of the treatment was assessed using a combination of:

    • Photographic assessment: Blind evaluation of pre and post-treatment photographs.
    • Ultrasound imaging: Measurement of percentage change in adipose tissue thickness from baseline to 12-week follow-up.

    8. Sample Size for the Training Set

    The document describes a clinical study to evaluate the device's safety and efficacy for specific body areas. This study serves as the primary evidence for the device's performance. The concept of a "training set" typically applies to machine learning models. For a physical device like SculpSure, the provided documentation does not refer to a separate "training set" in the context of clinical studies.

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

    As mentioned above, the concept of a "training set" as it applies to software or AI algorithms is not directly relevant here. The "ground truth" for the clinical study's effectiveness was established through the objective measurements of ultrasound imaging for fat reduction and the subjective yet blinded assessment of photographic changes.

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