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510(k) Data Aggregation

    K Number
    K043374
    Date Cleared
    2005-02-16

    (70 days)

    Product Code
    Regulation Number
    888.3565
    Reference & Predicate Devices
    N/A
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Duracon® Knee System Patella components included in this submission are intended for use in total knee arthroplasty to relieve pain and restore knee functions for indications such as:

    • Noninflammatory degenerative joint disease including osteoarthritis; traumatic arthritis or ● avascular necrosis;
    • . Rheumatoid arthritis;
    • Correction of functional deformity; .
    • Revision procedures where other treatments or devices have failed; .
    • Post traumatic loss of joint anatomy, particularly when there is patello-femoral erosion, . dysfunction or prior patellectomy; and,
    • Irreparable fracture of the knee. ●

    These products are intended to achicve fixation without the use of bone cement.

    Device Description

    The device patellar components of a total knee system. These components are used for the replacement of the articulating surfaces of the patella to relieve pain, instability and the restriction of motion due to degenerative bone disease, including osteoarthritis, rheumatoid arthritis, failure of other devices or trauma.

    AI/ML Overview

    This document is a 510(k) premarket notification from the FDA, primarily focusing on determining substantial equivalence for a medical device (Duracon® Conversion and X-Small Patella with Peri-Apatite® Coating) to a predicate device. As such, it does not contain the kind of detailed information typically found in a clinical study report that directly describes acceptance criteria, device performance, and study methodologies with sample sizes, expert qualifications, or detailed ground truth establishment for an AI/CADe device.

    The document mentions "risk analysis and research and development testing" to demonstrate equivalence. It also lists several types of testing performed, but these are engineering and material science tests, not clinical studies in the context of AI/CADe performance.

    Therefore, most of the requested information cannot be extracted from this document, as it pertains to a different type of device approval (a knee implant) and a different methodology (510(k) substantial equivalence based on engineering and material testing, not AI performance).

    Here's a breakdown of what can be inferred or is directly stated, and what cannot be found:

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

    • Cannot be provided in the context of AI/CADe performance. The document describes engineering tests for a knee implant. The "performance" being assessed is mechanical and material integrity, not diagnostic accuracy in an AI sense.
    • What is mentioned: "risk analysis and research and development testing" including "porous coating characterization, contact area / stress analyses; range of motion range of constraint testing; locking mechanism testing; UHMWPE material properties... safety testing for the Peri-Apatite coating."
    • Implied Acceptance Criteria (for the engineering tests): The tests were performed "in accordance with the Class II Special Controls Guidance Document: Knee Joint Patellofemorotibial and Femorotibial Metal/Polymer Porous-Coated Uncemented Prostheses; Guidance for Industry and FDA," dated January 16, 2003. This guidance document would contain the specific acceptance criteria for each test.
    • Reported Device Performance (for the engineering tests): "The results demonstrate that the Duracon Conversion Patella with Porous and Peri-Apatite coatings is safe and effective for use in total knee replacement without bone cement." This is a summary statement, not detailed quantitative performance data as one would expect for an AI device.

    2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

    • Cannot be provided. This document does not describe a clinical study in the typical sense of testing a diagnostic algorithm against a patient dataset. The "testing" refers to mechanical and material characterization of the knee implant.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

    • Cannot be provided. This is not relevant to the type of device and testing described.

    4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

    • Cannot be provided. Not relevant.

    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, it was not done. This document pertains to a knee implant, not an AI/CADe system.

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

    • No, this is not an AI algorithm.

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

    • Not applicable in the AI/CADe sense. For the engineering tests, the "ground truth" would be established by validated test methods and physical measurements comparing the device's properties against established material science and biomechanical standards defined in the guidance document.

    8. The sample size for the training set:

    • Cannot be provided. This is not an AI device that would have a "training set."

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

    • Cannot be provided. Not applicable.

    In summary: This document is an FDA 510(k) clearance letter for a knee implant. The "study" mentioned refers to engineering and material testing to demonstrate substantial equivalence to a predicate device, not a clinical performance study of an AI/CADe system. Therefore, the specific information requested about acceptance criteria, sample sizes, expert involvement, and ground truth for AI performance is not present in this document.

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