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510(k) Data Aggregation

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

    For treatment of 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 glenoid baseplate is intended for cementless application with the addition of screws for fixation. The humeral stem is intended for cemented use only.

    Device Description

    The modification consists of creating a monoblock device by joining the humeral socket with the humeral stem and to add additional humeral insert sizes. There is no change to the intended use or fundamental scientific technology of the RSP with the modifications in this Special 510(k) submission. This includes no changes to packaging or sterilization.

    AI/ML Overview

    This document is a 510(k) premarket notification for a medical device called the "Reverse Shoulder Prosthesis". It states that no clinical testing was performed. The submission relies on non-clinical mechanical testing and a comparison to predicate devices to demonstrate substantial equivalence.

    Therefore, the requested information about acceptance criteria and a study proving the device meets those criteria, specifically concerning performance with human data, cannot be extracted from this document.

    The document does provide information relevant to a non-clinical study, which is included below for completeness:

    1. Table of Acceptance Criteria and Reported Device Performance (Non-Clinical)

    Acceptance Criteria (Non-Clinical)Reported Device Performance (Non-Clinical)
    Device's ability to perform under expected clinical conditions (implied for mechanical testing)Mechanical testing demonstrated the device's ability to perform under expected clinical conditions.
    Equivalent characteristics to the predicate device (implied for various analyses)All activities (geometric analysis, socket lever out strength, stress analysis, tolerance analysis, plasma coating characterization, material properties review, design comparison) demonstrate that the modified device is substantially equivalent to the predicate.

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

    • Test Set Sample Size: Not applicable, as this was non-clinical testing. The "test set" consisted of physical prototypes and design specifications for mechanical analysis.
    • Data Provenance: Not applicable for clinical data. The data originates from internal engineering analysis and mechanical testing performed by the manufacturer (Encore Medical, L.P. / DJO Surgical).

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

    • Not applicable as this was non-clinical mechanical and design equivalence testing, not clinical human performance evaluation. The "ground truth" was established by engineering specifications, material science standards, and accepted biomechanical testing methodologies.

    4. Adjudication method for the test set

    • Not applicable as this was non-clinical testing. Verification activities were performed, implying internal review and assessment against design specifications and predicate device characteristics.

    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. This was a non-clinical submission for a shoulder prosthesis, not an AI-based diagnostic or treatment device involving human readers.

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

    • No. This was a non-clinical submission for a shoulder prosthesis, not an algorithm.

    7. The type of ground truth used

    • For the non-clinical testing, the "ground truth" was based on:
      • Engineering design specifications.
      • Material property standards.
      • Biomechanical principles and accepted testing methodologies (e.g., for socket lever out strength, stress analysis).
      • Characteristics and performance of the legally marketed predicate devices.

    8. The sample size for the training set

    • Not applicable. There was no machine learning or AI involved requiring a training set.

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

    • Not applicable. There was no machine learning or AI involved requiring a training set.
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