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510(k) Data Aggregation

    K Number
    K173073
    Date Cleared
    2017-12-26

    (88 days)

    Product Code
    Regulation Number
    888.3660
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    AltiVate Anatomic to Reverse Conversion Module

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

    The AltiVate Anatomic™ to Reverse Conversion Module is indicated for revision surgeries in patients with a grossly rotator cuff deficient shoulder joint with severe arthropathy or a previously failed joint replacement with a grossly rotator cuff deficient shoulder joint. The patient's joint must be anatomically and structurally suited to receive the selected implant(s), and a functional deltoid muscle is necessary to use the device. The conversion module is only indicated for use with a well fixed AltiVate Anatomic Humeral Stem.
    Humeral components with a porous coated surface are indicated for either cemented or uncemented applications. The glenoid baseplate is intended for cementless application with the addition of screws for fixation.

    Device Description

    In cases of revision surgeries to a well fixed AltiVate Humeral Stem, a reverse conversion module can be mated with the AltiVate stem to convert to a reverse shoulder application.
    The currently cleared AltiVate™ Anatomic humeral stem will a conversion module to provide an option to revise a failed traditional total shoulder arthroplasty to a reverse shoulder arthroplasty without the need to remove a well fixed humeral stem.

    AI/ML Overview

    The provided text describes a 510(k) premarket notification for the "AltiVate Anatomic to Reverse Conversion Module," a medical device. The document states that clinical testing was not required or performed for this submission. Therefore, it does not contain information regarding acceptance criteria, device performance from clinical studies, sample sizes, ground truth establishment, or multi-reader multi-case studies.

    The document primarily focuses on non-clinical testing and substantial equivalence to predicate devices.

    Here's a breakdown of the available information based on the provided input and the limitations due to the absence of clinical testing:

    1. A table of acceptance criteria and the reported device performance:

    • No clinical acceptance criteria or reported clinical device performance are provided as clinical testing was not performed.
    • Non-clinical testing: FEA analysis for stress analysis was conducted, determining the subject device is similar to the evaluated predicate device. Bacterial endotoxin testing was also conducted and met expected limits. However, specific numerical acceptance criteria or performance metrics for these non-clinical tests are not detailed in the provided text.

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

    • Not applicable as no clinical test set was used.

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

    • Not applicable as no clinical test set was used.

    4. Adjudication method for the test set:

    • Not applicable as no clinical test set was used.

    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 MRMC study was done. This device is a medical implant, not an AI diagnostic tool.

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

    • Not applicable. This device is a medical implant, not an AI algorithm.

    7. The type of ground truth used:

    • For the non-clinical tests, the "ground truth" would be engineering specifications and established standards for mechanical performance and sterility (endotoxin levels). The document states the FEA analysis found the device "similar to the evaluated predicate device" and endotoxin testing "met the expected endotoxin limits."

    8. The sample size for the training set:

    • Not applicable as this is not an AI/algorithmic device and no clinical "training set" was used.

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

    • Not applicable as this is not an AI/algorithmic device and no clinical "training set" was used.

    In summary, the FDA cleared this device based on non-clinical testing (FEA analysis, endotoxin assessment) and demonstration of substantial equivalence to predicate devices, without requiring or performing clinical trials.

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