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510(k) Data Aggregation

    K Number
    K151747
    Device Name
    STERISPINE PS
    Manufacturer
    SAFE ORTHOPAEDICS
    Date Cleared
    2015-10-05

    (98 days)

    Product Code
    NKB,KWP,MNH,MNI
    Regulation Number
    888.3070
    Type
    Traditional
    Panel
    Orthopedic
    Reference & Predicate Devices
    K130632,K121299,K112453,K140802,K150092
    Predicate For
    K170528,K201648
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The SteriSpine™PS system is intended to provide immobilization of spinal segments in skeletally mature patients as an adjunct to fusion. SteriSpine™PS System is intended for posterior, non-cervical pedicle and non-pedicle fixation for the following indications: degenerative disc disease (DDD) (defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies); spondylolisthesis; trauma (i.e., fracture or dislocation); spinal stenosis; tumor; pseudoarthrosis; and failed previous fusion.

    Device Description

    SteriSpineTMPS system includes pedicle screws (multiaxial and cannulated) and rods (straight and prebent). Implantable components of SteriSpineTMPS system are made of Titanium Ta6V Eli grade conforming to ASTM F136. The SteriSpineTMPS range of products is supplied sterile with a sterile single-use set of surgical instruments.

    AI/ML Overview

    I am sorry, but the provided text is a 510(k) summary for a medical device (SteriSpine™ PS) and focuses on regulatory clearance, not on a study that establishes acceptance criteria for performance metrics of an AI/ML powered device.

    The document discusses:

    • The device's indications for use.
    • Its classification and regulatory details.
    • Comparisons to previously cleared predicate devices.
    • Changes made to surgical instruments and packaging.
    • Non-clinical tests performed (biocompatibility, packaging validation, sterilization revalidation) to ensure conformance to standards.

    There is no mention of:

    • Acceptance criteria related to AI/ML performance metrics (e.g., sensitivity, specificity, accuracy).
    • A study with a test set, ground truth, expert readers, or training set relevant to an AI/ML algorithm's performance.
    • A multi-reader multi-case (MRMC) comparative effectiveness study or standalone algorithm performance.

    Therefore, I cannot extract the requested information regarding acceptance criteria and a study proving a device meets them, as this information is not present in the provided text.

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