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510(k) Data Aggregation

    Why did this record match?
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Materialise TKA Guide System is intended to be used as a surgical instrument to assist in the positioning of total knee replacement components intra-operatively and in guiding the marking of bone before cutting provided that anatomic landmarks necessary for alignment and positioning of the implant are identifiable on patient imaging scans.

    The Materialise TKA Guide System is to be used with Zimmer NexGen CR-Flex fixed bearing, Zimmer NexGen CR fixed bearing, Zimmer NexGen LPS-Flex fixed bearing, Zimmer NexGen LPS fixed bearing, Zimmer Gender Solutions Natural - Knee Flex fixed bearing, Zimmer Persona™ CR fixed bearing, Zimmer Persona™ PS fixed bearing, Zimmer Persona™ Trabecular Metal™, Vanguard® Complete Knee System, Vanguard® SSK 360, Vanguard® SSK Revision Knee System, Regenerex® Primary Tibial System, Offset & Microplasty Tibial Systems, Maxim® Complete Knee System, Ascent™ Total Knee System, and AGC® Complete Knee system, and Consensus Knee System prostheses families only.

    The Materialise TKA Guides are intended for single use only.

    Device Description

    Materialise TKA Guides are patient-specific medical devices that are designed to implant the following knee prostheses:

    Zimmer NexGen CR-Flex fixed bearing, Zimmer NexGen CR fixed bearing, Zimmer NexGen LPS-Flex fixed bearing, Zimmer NexGen LPS fixed bearing, Zimmer Gender Solutions Natural - Knee Flex fixed bearing, Zimmer Persona™ CR fixed bearing, Zimmer Persona™ PS fixed bearing, Zimmer Persona ™ Trabecular Metal ™, Vanguard® Complete Knee System, Vanguard® SSK 360, Vanguard® SSK Revision Knee System, Regenerex® Primary Tibial System, Offset & Microplasty Tibial Systems, Maxim® Complete Knee System, Ascent™ Total Knee System, and AGC® Complete Knee system, and Consensus Knee System.

    The Materialise TKA Guides must only be used in conjunction with the implants listed above. Consult the prosthesis labeling and instructions for use for specific patient indications, associated risks, information for use, warnings and precautions. Materialise TKA Guides is an instrument set containing a femur and/or tibia template(s).

    AI/ML Overview

    The provided text describes a 510(k) premarket notification for the Materialise TKA Guide System. While it states that "non-clinical tests using saw bones" and "Cadaver testing" were performed to verify the system's adequacy and demonstrate equivalent product performance, it does not provide specific quantitative acceptance criteria or detailed results of a study designed to prove the device meets those criteria.

    Therefore, many of the requested details cannot be extracted from this document.

    Here's what can be inferred and what is missing:

    1. Table of acceptance criteria and reported device performance:

    The document mentions "accuracy and performance of the system is adequate to perform as intended" and "demonstrated equivalent product performance as the existing predicate devices," but it does not specify what those performance metrics or their acceptance criteria are.

    Acceptance CriteriaReported Device Performance
    (Not specified in the document)"accuracy and performance of the system is adequate to perform as intended"
    (Not specified in the document)"equivalent product performance as the existing predicate devices (K102795 and K133162)"

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

    • Sample Size: Not specified for "saw bones" or "cadaver testing."
    • Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). The tests were non-clinical, so human patient data provenance is not applicable here.

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

    This information is not provided. The term "ground truth" as typically used for AI/ML validation (e.g., expert consensus) is not directly applicable to the non-clinical hardware testing described.

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

    This information is not provided.

    5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

    No, an MRMC comparative effectiveness study is not mentioned. The studies described are non-clinical (saw bones, cadaver) rather than clinical studies involving human readers or decision-making.

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

    The document mentions "non-clinical tests using saw bones" and "Cadaver testing." These tests evaluate the physical guide's accuracy in transferring the surgical plan (generated by the software) to the bone. This represents a form of standalone performance evaluation for the physical guide's output accuracy based on the plan. However, the performance of the software algorithm itself in producing the initial plan is not detailed as a separate standalone study with specific metrics. The software is used "pre-operatively by a qualified surgeon to inspect, fine-tune and approve the pre-surgical plan," implying human-in-the-loop for the planning phase.

    7. The type of ground truth used:

    For the "saw bones" and "cadaver testing," the ground truth would likely be based on physical measurements of the actual cut/drill locations relative to the intended surgical plan. This is a form of physical measurement/dimensional accuracy rather than expert consensus, pathology, or outcomes data typically used for diagnostic or predictive AI.

    8. The sample size for the training set:

    The document describes the Materialise TKA Guide System, which involves software for pre-surgical planning and 3D printed surgical guides. It doesn't explicitly mention an AI/ML component with a separate "training set" in the context of a typical AI/ML development pipeline. The "Materialise TKA Planner" is software device, but its development methodology (e.g., machine learning training) is not detailed.

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

    Not applicable, as a discrete training set and its ground truth establishment are not described for an AI/ML model.

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