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510(k) Data Aggregation

    K Number
    K231884
    Device Name
    TRIOCLEAR System
    Date Cleared
    2023-09-01

    (66 days)

    Product Code
    Regulation Number
    872.5470
    Panel
    Dental
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The TRIOCLEAR System is a series of clear, lightweight, plastic appliances indicated for the treatment of tooth malocclusions in patients with permanent dentition (i.e. all second molars). Utilizing a series of increment, it sequentially positions teeth by way of continuous gentle force.

    Device Description

    The TRIOCLEAR System is a removable, non-sterile device intended for single patient use. TRIOCLEAR is a series of clear plastic aligners that offer a solution for patients who want an aesthetic orthodontic treatment by utilizing sets of removable aligners to correct tooth malocclusions without the use of conventional wire and bracket orthodontic technology.

    AI/ML Overview

    The provided text describes the TRIOCLEAR System, an orthodontic device. However, it does not include the information requested regarding acceptance criteria and a study proving the device meets those criteria in the context of an AI/ML-driven device.

    The document is a 510(k) summary for the TRIOCLEAR System, which is a series of clear plastic aligners. The submission aims to establish substantial equivalence to a predicate device, primarily due to changes in raw material and thickness types. The performance testing section focuses on material properties of the thermoforming sheet, not the performance of an AI/ML component.

    Therefore, I cannot provide the requested information from the given text. The text does not discuss:

    • Acceptance criteria for an AI/ML device's performance.
    • A study proving an AI/ML device meets acceptance criteria.
    • Sample size for test sets or data provenance for AI/ML validation.
    • Details about experts for ground truth establishment.
    • Adjudication methods.
    • MRMC studies or effect sizes for AI assistance.
    • Standalone AI algorithm performance.
    • Type of ground truth (expert consensus, pathology, outcomes) in the context of AI.
    • Training set sample size or ground truth establishment for a training set for an AI/ML model.
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