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510(k) Data Aggregation

    K Number
    K981274
    Manufacturer
    Date Cleared
    1998-04-29

    (21 days)

    Product Code
    Regulation Number
    888.3070
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    ACROMED TIMX PLATE BASED LOW BACK SYSTEM

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

    The Titanium MX Plate Based Low Back System is intended for use in Grade 3 and 4 spondylolisthesis at L5-S1, when affixed to the lumbosacral spine, utilizing autologous bone graft, and intended to be removed after solid fusion is attained. Levels of attachment for this indication range from L3 to the sacrum.

    Device Description

    The TiMX Plate Based Low Back System is a variation of the existing titanium alloy VSP Spinal System previously cleared under K944736. The TiMX Plate Based Low Back System is a construct that consists of pedicle and sacral screws, spine plates, nuts, washers and a transverse connector. This modified system provides increased pedicle screw strength, increased construct fatigue performance, increased construct stiffness, improved geometry and lower profile than its predecessor, the titanium alloy VSP Spinal System.

    AI/ML Overview

    The document describes a medical device called the "TiMX Plate Based Low Back System" and its substantial equivalence to a previously cleared device. It is a 510(k) premarket notification, which assesses substantial equivalence to a predicate device rather than establishing new acceptance criteria and conducting studies to prove the device meets those criteria from scratch.

    Therefore, the information you're requesting regarding "acceptance criteria" and a "study that proves the device meets the acceptance criteria" in the context of typical AI/software device evaluation (e.g., sensitivity, specificity, F1-score) is not directly applicable to this type of medical device submission.

    Instead, the document details performance data in comparison to its predicate device to demonstrate substantial equivalence.

    Here's how to frame the information based on the provided text, focusing on the equivalence study presented:

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

    Since this is a 510(k) demonstrating substantial equivalence, the "acceptance criteria" are implied to be that the new device (TiMX) is "generally superior or equivalent" to the predicate device (standard titanium VSP system) in terms of its mechanical performance.

    Performance MetricAcceptance Criteria (Implied)Reported Device Performance (TiMX vs. Standard Titanium VSP)
    Torque plus bending performance (Pedicle Screw)Superior or Equivalent to predicateSignificant improvement in torque plus bending with VSP plates
    Torque to failure performance (Hexlobe feature)Superior or Equivalent to predicateBetter torque to failure performance
    Endurance limit (Pedicle Screw)Superior or Equivalent to predicate30% improvement in endurance limit
    Static compression bending (System)Superior or Equivalent to predicateGenerally superior or equivalent
    Static torsion (System)Superior or Equivalent to predicateGenerally superior or equivalent
    Dynamic compression bending (System)Superior or Equivalent to predicateGenerally superior or equivalent

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

    • Sample Size: Not specified in terms of distinct units or number of tests conducted, but refers to "a full battery of testing" on "both the TiMX and standard titanium VSP systems." This implies testing on physical prototypes of the components and systems.
    • Data Provenance: Not specified, but given it's a 510(k) submission in the US, the testing would typically be performed by the manufacturer or a certified lab, likely in the US, for regulatory submission to the FDA. The study is prospective in the sense that the testing was performed on the new device designs to gather data for the submission.

    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):

    This is not applicable. The "ground truth" here is objective mechanical engineering measurements (e.g., torque, bending force, fatigue cycles) from physical testing, not expert interpretation of medical data. Therefore, no human experts as described are involved in establishing ground truth for mechanical testing.

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

    Not applicable, as this is objective mechanical testing. The results would be quantitative measurements, not subjective evaluations 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 document describes a physical medical implant (spinal system), not an AI/software device that assists human readers.

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

    Not applicable. This is not an algorithm or software. It is a physical device.

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

    The "ground truth" for the performance claims is objective mechanical engineering measurements obtained from physical laboratory testing (e.g., static compression bending, static torsion, dynamic compression bending, torque to failure, endurance limit) comparing the new device components and system to the predicate device components and system.

    8. The sample size for the training set:

    Not applicable. This is a physical device, not a machine learning algorithm that requires a training set. The "training" in manufacturing comes from design iterations and engineering experience.

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

    Not applicable, as there is no training set in the context of an AI/ML algorithm for this device.

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