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510(k) Data Aggregation

    K Number
    K103243
    Manufacturer
    Date Cleared
    2011-01-21

    (81 days)

    Product Code
    Regulation Number
    888.3030
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    2.4MM VA-LCP INTERCARPAL FUSION SYSTEM

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

    The 2.4mm VA-LCP Intercarpal Fusion System is indicated for fusion of small bones of the hand including: hamate, capitate, lunate, and triquetrum, for the revision of failed partial wrist fusions, and is indicated for use in patients suffering pain and/or loss of function due to:

    • -Osteoarthritis
    • -Rheumatoid arthritis
    • -Post-traumatic or degenerative wrist arthritis
    • -Carpal instability
    Device Description

    The 2.4mm VA-LCP Intercarpal Fusion System features low profile circular plates. The plates are available in two sizes: ø15mm (6 holes) and øl 7mm (7 holes). The plates each feature variable angle locking technology. The plates also feature K-wire holes for positioning and temporary fixation.

    AI/ML Overview

    This document is a 510(k) summary for a medical device called the "2.4mm VA-LCP Intercarpal Fusion System." It describes a traditional medical device (bone fixation system), not a software or AI-powered device. Therefore, the questions related to AI/software performance, such as acceptance criteria based on AI metrics, sample sizes for test/training sets, expert ground truth, adjudication methods, and MRMC studies, are not applicable here.

    The "study that proves the device meets the acceptance criteria" in this context refers to the engineering and mechanical testing performed to demonstrate substantial equivalence to predicate devices. The acceptance criteria are implicit in showing that the new device performs comparably to or better than existing, legally marketed devices for its intended use.

    Here's the information as it applies to this specific document:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criteria (Implicit from Substantial Equivalence to Predicate Devices)Reported Device Performance (Summary in Document)
    Material Composition: Use of biocompatible and mechanically suitable materials comparable to predicate devices."The subject and predicate devices are made from stainless steel and titanium alloy (TAN)."
    Mechanical Strength & Functional Properties: Demonstrate comparable or superior mechanical strength and functional performance for its intended orthopedic application (e.g., bone fusion) to predicate devices."Functional and mechanical testing demonstrates the comparable mechanical & functional properties of the subject 2.4mm VA-LCP Intercarpal Fusion System to the predicate devices. In support of this submission 1-point bending was performed."
    Intended Use: Device is suitable for the specified indications for use."The 2.4mm VA-LCP Intercarpal Fusion System is indicated for fusion of small bones of the hand... for the revision of failed partial wrist fusions, and is indicated for use in patients suffering pain and/or loss of function due to: Osteoarthritis, Rheumatoid arthritis, Post-traumatic or degenerative wrist arthritis, Carpal instability."
    Design Characteristics: Features (plate size, hole count, locking technology, K-wire holes) are functionally equivalent to predicate devices."The plates are available in two sizes: ø15mm (6 holes) and øl 7mm (7 holes). The plates each feature variable angle locking technology. The plates also feature K-wire holes for positioning and temporary fixation." These features are presented as similar to predicate designs.
    Manufacturing, Packaging, Sterilization: Methods are identical or comparable to predicate devices."In addition, the intended use, manufacturing methods, packaging, and sterilization of the predicate and subject components are identical."

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

    • Not Applicable / Not Specified. This document describes a medical device (surgical implant) cleared based on substantial equivalence through mechanical testing, not a data-driven device evaluated with a test set of patient data. The "test set" would be the physical devices subjected to mechanical testing. The exact number of devices tested for 1-point bending is not specified in this summary.

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

    • Not Applicable. Ground truth in this context is established by engineering standards and direct physical measurements during mechanical testing, not by human expert interpretation of imaging or clinical data.

    4. Adjudication method for the test set

    • Not Applicable. Mechanical testing results are objective measurements against established engineering principles and predicate device performance, not subjective interpretations requiring 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 is not an AI/software device.

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

    • Not Applicable. This is not an AI/software device.

    7. The type of ground truth used

    • Engineering Standards and Physical Measurements: The "ground truth" for this device's performance is derived from established mechanical engineering principles, material science data, and direct physical measurements obtained through tests like 1-point bending. The acceptance criteria are implicitly met by demonstrating "comparable mechanical & functional properties" to legally marketed predicate devices.

    8. The sample size for the training set

    • Not Applicable. This is not a machine learning or AI device that requires a "training set."

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

    • Not Applicable. This is not a machine learning or AI device.
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