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510(k) Data Aggregation

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    Reference Devices :

    K071257, K080918, K072322, K073149

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

    The proposed OMNISPAN Meniscal Repair System is intended for use in the arthroscopic fixation of soft tissue procedures such as meniscal repair.

    Device Description

    The proposed OMNISPAN Meniscal Repair System consists of two sterile PEEK (polyetheretherketone) implants connected by a sliding loop and fixed leg of absorbable size #2-0 Orthocord suture, a sterile, disposable Deployment Gun with malleable Graft Retractor. The implants together with the suture provide compression across the tear in the meniscus.

    AI/ML Overview

    This submission describes the OMNISPAN Meniscal Repair System, a medical device for arthroscopic fixation of meniscal tears. The submission asserts substantial equivalence to previously cleared devices.

    1. Table of Acceptance Criteria and Reported Device Performance

    The provided document (K092836) is a 510(k) summary for a medical device seeking substantial equivalence, rather than a clinical study reporting specific performance metrics against pre-defined acceptance criteria. Therefore, a direct table of acceptance criteria and reported device performance, as would be found in a clinical trial report, is not present in this document.

    Instead, the document states: "Results of performance and safety testing have demonstrated that the modified device is suitable for its intended use." This general statement indicates that internal testing was conducted to support the device's suitability, but the specific acceptance criteria and detailed performance results are not disclosed in this summary. The FDA's decision to clear the device implies that the agency found these undisclosed tests satisfactory.

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

    This document does not describe a clinical test set with a specific sample size, nor does it detail data provenance (e.g., country of origin, retrospective/prospective). The submission relies on demonstrating substantial equivalence to predicate devices based on technological characteristics and indications for use, supported by internal performance and safety testing, the details of which are not provided in this summary.

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

    Given that no clinical test set is described in detail, there is no information provided regarding experts used to establish ground truth.

    4. Adjudication Method for the Test Set

    As no clinical test set is described, there is no information on any adjudication method.

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

    A multi-reader multi-case (MRMC) comparative effectiveness study was not conducted or reported in this 510(k) summary. The submission focuses on substantial equivalence based on device characteristics and predicate devices, not on direct comparison of human readers with and without AI assistance.

    6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

    This device is a physical medical implant (OMNISPAN Meniscal Repair System), not an AI algorithm. Therefore, a standalone (algorithm only) performance study is not applicable and was not reported.

    7. Type of Ground Truth Used

    The document does not describe a specific "ground truth" as would be used in an AI or diagnostic device study. The "ground truth" here pertains to the device's functionality and safety, which would have been established through internal engineering tests, biocompatibility assessments, and mechanical testing, rather than expert consensus on diagnostic images or pathology.

    8. Sample Size for the Training Set

    This device is a physical medical system; it does not involve an AI algorithm with a training set. Therefore, this question is not applicable.

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

    As this device does not involve an AI algorithm, the concept of a "training set" and its associated ground truth is not applicable.

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