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510(k) Data Aggregation

    K Number
    K143230
    Date Cleared
    2015-02-25

    (107 days)

    Product Code
    Regulation Number
    888.3070
    Why did this record match?
    Device Name :

    Palladian Lumbar Pedicle Screw System

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

    The use of the Palladian™ Lumbar Pedicle Screw System is indicated as an adjunct to fusion of the L5-S1 vertebra for the treatment of; degenerative disc disease (defined as discogenic back pain with degeneration of the disc confirmed by history and radiographic studies), spondylolisthesis, trauma (i.e., fracture or dislocation), deformity, or curvature (i.e., scoliosis, kyphosis, and lordosis), tumor, stenosis, pseudoarthrosis, and previous failed fusion.

    The Palladian™ Lumbar Pedicle Screw System is a non-cervical spinal fixation system, and intended for use with autograft and/or allograft. Pedicle screw fixation is limited to skeletally mature patients.

    Device Description

    The Palladian™ Lumbar Pedicle Screw System is a multiple component, top-loading, posterior spinal fixation system which consists of pedicle screws, rods, cross-connectors, and locking cap set screws. All of the components are available in a variety of sizes to match more closely to the patient's anatomy. All components are made from titanium alloy described by such standards as ASTM F136.

    AI/ML Overview

    This document is a 510(k) Premarket Notification summary for the Palladian™ Lumbar Pedicle Screw System. It describes the device, its indications for use, its technological characteristics compared to predicate devices, and the performance data that supports its substantial equivalence.

    Here's an analysis of the provided information, focusing on the acceptance criteria and study details:

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

    The document does not explicitly state "acceptance criteria" with numerical thresholds. Instead, it describes performance testing that aims to demonstrate substantial equivalence to legally marketed predicate devices. The reported performance is implicitly that the device's strength is "sufficient for its intended use" and "substantially equivalent" to predicate devices.

    Performance TestAcceptance Criteria (Implicit)Reported Device Performance
    Static axial compression bending per ASTM F1717-13Strength sufficient for intended use and substantially equivalent to predicate devicesResults show strength is sufficient and substantially equivalent
    Static torsion per ASTM F1717-13Strength sufficient for intended use and substantially equivalent to predicate devicesResults show strength is sufficient and substantially equivalent
    Dynamic axial compression bending fatigue per ASTM F1717-13Strength sufficient for intended use and substantially equivalent to predicate devicesResults show strength is sufficient and substantially equivalent

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

    The document describes non-clinical mechanical testing of the device components. For such tests, the "sample size" refers to the number of device samples tested, not a population of patients. The document does not specify the exact number of samples (e.g., screws, rods) tested for each mode. It solely states "The Palladian™ Lumbar Pedicle Screw System has been tested in the following test modes."

    • Sample Size: Not specified (refers to device components for mechanical testing, not patients).
    • Data Provenance: Not explicitly stated, but as mechanical testing of a medical device, it is typically conducted in a controlled laboratory environment. The country of origin for the data is not mentioned, nor is it explicitly retrospective or prospective, as these terms usually apply to clinical studies involving human subjects.

    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 section is not applicable to this document. The provided text describes mechanical performance testing of a spinal implant, not a diagnostic or AI-driven device that requires expert-established ground truth from patient data. The "ground truth" here is the physical performance of the device under specific loads according to industry standards.

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

    This section is not applicable for the same reason as point 3. Adjudication methods like 2+1 or 3+1 are used in clinical studies or evaluations of diagnostic accuracy where human experts assess cases and resolve discrepancies. This document describes non-clinical mechanical testing.

    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

    This section is not applicable. An MRMC study is relevant for evaluating the performance of diagnostic devices, especially those involving human interpretation of data, often with AI assistance. This document is about the mechanical performance of a spinal implant.

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

    This section is not applicable. This question pertains to the performance of an algorithm or AI system. The device in question is a physical spinal implant, not a software algorithm.

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

    The "ground truth" for the mechanical testing is the physical properties and performance of the device components as measured against established engineering standards (ASTM F1717-13). There is no "expert consensus" or "pathology" in the sense of clinical diagnoses needed here. The standard itself defines the acceptable criteria for performance.

    8. The sample size for the training set

    This section is not applicable. The device is a physical implant, not a machine learning model that requires a training set.

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

    This section is not applicable for the same reason as point 8.

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