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510(k) Data Aggregation

    K Number
    K061853
    Date Cleared
    2006-08-18

    (49 days)

    Product Code
    Regulation Number
    874.3450
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    GRACE MEDICAL ADJUSTABLE AND FIXED LENGTH PARTIAL & TOTAL OSSICULAR REPLACEMENT PROSTHESES

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

    An ossicular replacement prosthesis is a device intended to the functional reconstruction of segments of the ossicular chain and facilitates the conduction of sound wave from the tympanic membrane to the inner ear.

    Ossicular replacement prostheses are indicated for the function of the ossicular chain when a conductive hearing loss is present. Indications for use include: Chronic middle ear disease, Otosclerosis, Congenital fixation of the stapes, Secondary surgical intervention to correct for a significant and persistent conductive hearing loss from prior otologic surgery, and surgically correctible injury to the middle ear from trauma.

    Device Description

    The family of Grace Medical Adjustable and Fixed Length Partial & Total Ossicular Replacement Prostheses consists of: Grace Medical ALTO (Adjustable Length Total Ossicular) Prostheses Grace Medical ALPO (Adjustable Length Partial Ossicular) Prostheses Grace Medical FLTO (Fixed Length Total Ossicular) Prostheses Grace Medical FLPO (Fixed Length Partial Ossicular) Prostheses Silverstein ALTO (Adjustable Length Partial Ossicular) Prostheses with Footplate Tack Grace Medical Frisbee Myringopexy (Fixed Length Partial Ossicular) Prostheses. The Grace Medical Adjustable Length and Fixed Length Partial & Total Ossicular Replacement Prostheses are manufactured from the same or similar materials as the predicate devices. The Grace Medical Adjustable-Length PORP's and TORP's contain a medical grade silicone sleeve fixed to the titanium shaft which allows for adjustment to length by the user. Various designs of Grace Medical Adjustable Length Partial & Total Ossicular Replacement Prostheses are available to meet physician preference.

    AI/ML Overview

    This is a 510(k) premarket notification for a medical device (Grace Medical Adjustable Length and Fixed Length Partial & Total Ossicular Replacement Prostheses), not a study evaluating software performance or an AI/ML device. Therefore, much of the requested information (acceptance criteria for device performance, sample sizes for test/training sets, expert ground truth, adjudication methods, MRMC studies, standalone algorithm performance, and effect sizes) is not applicable or cannot be extracted from this document.

    The document focuses on establishing substantial equivalence to predicate devices based on intended use, materials, and design features, rather than presenting a performance study against specific acceptance criteria for a new, unique device function.

    Here's a breakdown of the applicable information:

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

    This document does not specify quantitative "acceptance criteria" for performance that would typically be found in a study for a new device's efficacy (e.g., a certain percentage improvement in hearing). Instead, the "acceptance" for this 510(k) is regulatory approval based on demonstrating substantial equivalence to legally marketed predicate devices.

    The "reported device performance" is essentially that it has the "same primary intended use" and that "Differences between the Grace Medical Modified Partial Ossicular Replacement Prostheses and the predicate devices should not raise new issues regarding safety or effectiveness." This statement serves as the qualitative assessment of its performance relative to existing devices.

    The core "acceptance criteria" for a 510(k) submission are met if the device:

    • Has the same intended use as a predicate device.
    • Has the same technological characteristics as the predicate device; OR
    • Has different technological characteristics from a predicate device AND the information submitted to FDA does not raise new questions of safety and effectiveness AND the device is as safe and effective as the predicate device.

    The document implicitly "reports" that these criteria have been met through the comparative tables and the FDA's final letter of substantial equivalence.

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

    Not applicable. This is a 510(k) submission based on substantial equivalence to existing devices, not a clinical performance study using a test set of data.

    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)

    Not applicable. No ground truth for a test set was established as part of this submission for direct device performance evaluation.

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

    Not applicable. No test set adjudication.

    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

    Not applicable. This device is an implantable prosthesis, not an AI-assisted diagnostic tool or an imaging device.

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

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

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

    Not applicable. The "ground truth" for this submission is the established safety and effectiveness of the predicate devices based on their prior regulatory clearance and intended use.

    8. The sample size for the training set

    Not applicable. There is no "training set" in the context of an AI/ML algorithm for this type of device submission.

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

    Not applicable. No training set for an algorithm.

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