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510(k) Data Aggregation

    K Number
    K230445
    Device Name
    OnX Tough
    Manufacturer
    Date Cleared
    2023-07-14

    (143 days)

    Product Code
    Regulation Number
    872.3760
    Panel
    Dental
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    SprintRay OnX Tough is a tooth shade ceramic-hybrid resin used for the fabrication of hybrid denture prosthetics, implant-supported denture prosthetics, monolithic full and partial removable dentures, and preformed denture teeth to be used in a denture.

    Device Description

    OnX Tough resin consists of a curable dental acrylate resin that is manufactured in a dental office based on a 3D scanned image of a patient's teeth. The acrylate resin material is designed to be used in conjunction with a scanned 3D image, and 3D printer assembly, to locally manufacture out a dental appliance based on the clinician's judgment of patient need.

    Fabrication of dental prosthetics with OnX Tough resin requires computer-aided design and CAD/CAM manufacturing system that includes the following components not part of the device: oral casting impression, digital denture file created in an optical impression system, 3D printer, and curing light equipment.

    The material is an alternative to traditional dental prostheses material. OnX Tough resin is intended exclusively for professional dental work.

    OnX Tough resin is designed to meet appropriate ISO standards for flexibility and sorption, to withstand prolonged use in the oral cavity. It is delivered non-sterile, and instructions are provided on cleaning the material prior to providing it to a patient. Curing is performed with a UV lamp. The appliance is then cleaned, trimmed, and verified to fit in the dental office before the patient leaves.

    AI/ML Overview

    Here's an analysis of the provided text regarding the acceptance criteria and study for the SprintRay OnX Tough device.

    It's important to note that this document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices, not typically on a standalone clinical study demonstrating the device's diagnostic performance against a "ground truth" established by experts, as would be common for AI/ML devices. Therefore, many of the requested points from your prompt (like sample size for test sets, expert qualifications, MRMC studies, standalone algorithm performance, training set details) are not applicable in this context as this is a dental material, not an AI/ML diagnostic device.

    Here's a breakdown of the available information:


    1. Table of Acceptance Criteria and Reported Device Performance

    The device, OnX Tough, was tested against industry consensus standards, primarily ISO 20795-1 and ISO 20795-2, which set the acceptance criteria for denture base polymers. Biocompatibility testing was also conducted according to ISO 10993-1, among others.

    Performance MetricAcceptance Criteria (Standard Reference)Reported OnX Tough Performance
    BiocompatibilityISO 10993-1, -3, -5, -10, -11Passed
    Flexural Strength≥65 MPa (ISO 20795-1)71.03 ± 1.45 MPa
    Flexural Modulus≥2000 MPa (ISO 20795-1)2271 ± 118 MPa
    Sorption≤32 µg/mm³ (ISO 20795-2)31 ± 1 µg/mm³
    Solubility≤5 µg/mm³ (ISO 20795-2)4.0 ± 0.5 µg/mm³
    Residual Methyl Methacrylate Monomers≤2.2% (ISO 20795-1)Pass
    Kmax≥1.9 MPa*m^(1/2) (ISO 20795-1)3.176 ± 0.209
    Total Fracture Work≥900 J/m² (ISO 20795-1)945 ± 88 J/m²
    Other Bench TestsISO 20795-1 (various)Functioned as intended

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

    The document does not specify the exact sample sizes (e.g., number of test specimens for each mechanical test) used for the bench testing. This information is typically detailed in the full test reports, not usually in the 510(k) summary. Given this is a material properties verification, the "data provenance" is typically laboratory testing results, not patient data from a specific country or collected retrospectively/prospectively.


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

    Not Applicable. This is a material science and biocompatibility assessment, not a diagnostic device relying on expert interpretation of results to establish ground truth for a clinical condition. The "ground truth" for these tests is the quantitative measurement against established physical/chemical standards.


    4. Adjudication Method for the Test Set

    Not Applicable. As above, this is laboratory bench testing against objective criteria, not a clinical study requiring adjudication of expert interpretations.


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

    Not Applicable. This is a dental material, not an AI-assisted diagnostic tool. Therefore, MRMC studies evaluating human reader improvement with or without AI assistance are irrelevant to this device.


    6. Standalone (i.e., algorithm only without human-in-the-loop performance) Study

    Not Applicable. This is a physical dental material, not an algorithm or software. Its performance is evaluated through material property testing and biocompatibility.


    7. Type of Ground Truth Used

    The "ground truth" used for this device is based on established international standards for physical, mechanical, and chemical properties of dental materials (ISO 20795 series) and biocompatibility standards (ISO 10993 series). These are objective, quantitative benchmarks, not subjective expert consensus, pathology, or outcomes data in the sense of a diagnostic medical device.


    8. Sample Size for the Training Set

    Not Applicable. This is a manufactured dental resin, not a machine learning model that requires a training set. The development of the resin would involve material science research and iterative formulation, not data training in the context of AI.


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

    Not Applicable. As explained above, there is no "training set" in the context of an AI/ML algorithm for this device. The material's formulation and properties are developed through chemical engineering and material science principles, with validation against performance specifications derived from ISO standards.

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