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510(k) Data Aggregation

    K Number
    K110838
    Manufacturer
    Date Cleared
    2011-04-21

    (27 days)

    Product Code
    Regulation Number
    888.3040
    Reference & Predicate Devices
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The iFuse System is intended for sacroiliac joint fusion for conditions including sacroiliac joint disruptions and degenerative sacroiliitis.

    Device Description

    The SI Fusion System consists of a series of metallic (titanium), porous plasma spray coated rods, intended for surgical implant within the bone to create fixation. The system includes 4.0 mm and 7.0 mm diameter fusion rods, which range in length from 30 mm to 70 mm.

    AI/ML Overview

    The provided 510(k) summary for the SI-Bone's iFuse SI Fusion System (K110838) is for a medical device that does not involve AI or machine learning. Therefore, many of the requested categories related to AI development and validation are not applicable.

    The submission is for a physical medical device: a sacroiliac joint fusion system. The "performance data" section in the summary refers to mechanical and cadaveric testing to establish substantial equivalence to predicate devices, not to algorithmic performance.

    Here's a breakdown based on the provided document, clarifying where information is not applicable due to the nature of the device:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance CriteriaReported Device Performance
    Substantial Equivalence to Predicate Devices (SI-Bone, Inc's SI Joint Fusion System K080398; K092375)"Axial pull-out testing, Finite Element Analysis (FEA), and cadaver testing demonstrate that the iFuse is substantially equivalent with respect to fixation of the SI joint." The device has the "same intended use, principles of operation, and technological characteristics and similar indications for use as the predicate SI Joint Fusion System." "No differences in the SI Joint Fusion System's technological characteristics or principles of operation, thus the device does not raise any new questions of safety or effectiveness."
    Safety and Effectiveness"Performance data demonstrate that the SI Joint Fusion System is as safe and effective as previously cleared SI Joint Fusion System."

    2. Sample size used for the test set and the data provenance

    • Test Set Sample Size: Not specified in terms of number of samples for axial pull-out, FEA, or cadaver testing.
    • Data Provenance: Not specified (e.g., country of origin). The testing methods (axial pull-out, FEA, cadaver testing) are generally conducted in laboratory or research settings.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

    • Not applicable. This device is a physical implant. The "ground truth" for its performance is derived from biomechanical and material science testing, not from expert interpretation of data or images.

    4. Adjudication method for the test set

    • Not applicable. See point 3.

    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

    • Not applicable. This device does not involve AI, machine learning, or human "readers" in the context of interpretation.

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

    • Not applicable. This device does not involve an algorithm.

    7. The type of ground truth used

    • Ground truth for mechanical performance: Derived from established engineering and biomechanical testing standards (e.g., axial pull-out strength, FEA results for stress distribution, cadaveric studies for surgical fixation).

    8. The sample size for the training set

    • Not applicable. There is no "training set" in the context of AI for this physical medical device. The design and manufacturing would be based on engineering principles and prior knowledge of materials and biomechanics, rather than a data-driven training set.

    9. How the ground truth for the training set was established

    • Not applicable. See point 8.
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