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510(k) Data Aggregation

    K Number
    K170849
    Device Name
    SIMPLANT Guide
    Manufacturer
    Date Cleared
    2017-06-15

    (85 days)

    Product Code
    Regulation Number
    872.3980
    Panel
    Dental
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K993095, K993560

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

    The SIMPLANT® Guide is intended for use in assisting placement of dental implants.

    Device Description

    The SIMPLANT® Guide is intended for use in assisting placement of dental implants.

    It is a patient specific surgical template that is produced based upon knowledge of the location and orientation of the implant(s) prior to the surgery.

    The design of the SIMPLANT® Guide is made according to the clinician's plan of the implant positions. The final guide is fabricated from an epoxy resin using computerassisted manufacturing to produce a patient specific device.

    The SIMPLANT® Guide sits on the patient's oral anatomy, i.e. teeth, mucosa, bone or a combination thereof. Aided by the SIMPLANT® Guide, the implant sites can be prepared and the dental implants placed in the predetermined locations.

    AI/ML Overview

    Here's an analysis of the provided text regarding the acceptance criteria and study for the SIMPLANT® Guide:

    1. Table of Acceptance Criteria and Reported Device Performance

    The acceptance criteria are not explicitly stated with numerical thresholds in the provided document. Instead, the document indicates that the SIMPLANT® Guide meets the same acceptance criteria as its predicate device (SurgiGuide® guide). The "Results" column for the proposed SIMPLANT® Guide consistently reports "PASS", indicating it successfully met these unstated criteria.

    Bench Test PerformedImplied Acceptance Criteria (Shared with Predicate)Reported Device Performance (SIMPLANT® Guide)
    Tube Fixation Test- Push OutWithstands typical vertical loads during surgery.PASS
    Tube Fixation Test- TorqueWithstands occasional torque loads during surgery.PASS
    Bending TestWithstands typical bending loads during surgery.PASS
    Angulation Deviation TestManufactured according to its pre-operative plan regarding the angular position of the guiding tube.PASS
    Position Deviation TestManufactured according to its pre-operative plan regarding spatial position of the guiding tube.PASS
    Vertical Fit TestManufactured according to its pre-operative plan regarding the vertical position.PASS

    2. Sample Size for the Test Set and Data Provenance

    The document does not explicitly state the sample size used for each "bench test." It mentions "non-clinical performance tests have been developed in-house" and "were executed to demonstrate substantial equivalence."

    The data provenance is internal/in-house bench testing with no mention of country of origin of the data or whether it was retrospective or prospective in the context of human data. Given it's bench testing, it's a prospective series of tests conducted on the manufactured device.

    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. For bench testing, "ground truth" typically refers to engineering specifications, measurement standards, or the performance of a validated predicate device. The document states that the performance meets the "same acceptance criteria" as the predicate, implying the predicate's performance served as the benchmark.

    4. Adjudication Method for the Test Set

    This information is not provided. Given the nature of bench testing, the adjudication would likely be based on objective measurements and adherence to predefined test protocols and acceptance criteria, rather than a consensus method among human experts.

    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 conducted or described for the SIMPLANT® Guide. The device is a surgical guide, not an AI-powered diagnostic or assistive tool for human readers/clinicians in the way typically assessed by MRMC studies.

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

    This question is not directly applicable to the SIMPLANT® Guide in the context of typical AI algorithm performance. The SIMPLANT® Guide is a physical surgical template produced using computer-assisted manufacturing based on a clinician's plan. While "algorithm" might be involved in the manufacturing process (CAD/CAM), the device itself is a physical tool, not a standalone algorithm. The "standalone performance" in this context would be its physical accuracy and durability, which were tested via the bench tests (Tube Fixation, Bending, Angulation, Position, Vertical Fit).

    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

    For the bench tests, the ground truth or reference point appears to be:

    • Engineering Specifications/Pre-operative Plan: For the Angulation Deviation, Position Deviation, and Vertical Fit Tests, the "ground truth" is the device's design and how closely the manufactured product adheres to it.
    • Predicate Device Performance: The primary comparative "ground truth" is that the new device must meet the "same acceptance criteria" and perform identically to the predicate SurgiGuide® guide under the specified test conditions. This implies that the predicate's established performance serves as the benchmark for "acceptable" over typically applied loads.

    8. The Sample Size for the Training Set

    This information is not applicable/not provided. The SIMPLANT® Guide is a manufactured surgical template, not an AI model that requires a training set. The "design" (which might involve software or CAD) is based on the clinician's pre-operative implant plan, not a learned model from a large dataset.

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

    This information is not applicable/not provided for the same reasons as point 8.

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