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510(k) Data Aggregation

    K Number
    K053098
    Manufacturer
    Date Cleared
    2005-11-28

    (25 days)

    Product Code
    Regulation Number
    888.3045
    Reference & Predicate Devices
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    These products are indicated for bony voids or gaps that are not intrinsic to the stability of the bony structure. They are indicated to be placed into the bony voids or gaps of the skeletal system (e.g., extremities, spine, and pelvis). These defects may be surgically created osseous defects or osseous defects created from traumatic injury to the bone. These products provide a bone void filler that remodels into the recipient's skeletal system.

    Device Description

    These devices are comprised of sterile USP grade glycerol/glycerin and combined with human demineralized cortical bone at the point of manufacture.

    AI/ML Overview

    The provided text describes the submission for a 510(k) premarket notification for Optium DBM™ Gel and Putty. Instead of a study with acceptance criteria and device performance in numerical terms typically associated with AI/ML device studies, this submission relies on demonstrating substantial equivalence to predicate devices through non-clinical performance data and a history of safe use for similar products.

    Here's an analysis based on the provided text, adapted for the requested format:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criteria CategorySpecific CriteriaReported Device Performance
    Osteoinductive PotentialEqual to or more new bone formation than predicate devices in an in-vivo athymic mouse assay.Optium DBM™ Gel and Putty products had equal to or more new bone formation than the predicate device(s) assessed. This was evaluated by quantitative histomorphometric analysis of the explanted samples.
    Biocompatibility - Genotoxicity (Ref. ISO 10993-3)Pass Bacterial Mutagenicity Assay (Ames).Passed
    Pass In Vivo Mouse Micronucleus Assay.Passed
    Pass In Vitro Chromosomal Aberration Induction in Human Lymphocytes.Passed
    Biocompatibility - Cytotoxicity (Ref. ISO 10993-5)Pass MEM Elution (Product and packaging performed according to USP).Passed
    Biocompatibility - Sensitization (Ref. ISO 10993-10)Pass ISO Guinea Pig Maximization Sensitization Test (Gel and Glycerol).Passed
    Biocompatibility - Irritation (Ref. ISO 10993-10)Pass ISO Intracutaneous Reactivity Test.Passed
    Biocompatibility - Systemic Toxicity (Ref. ISO 10993-11)Pass ISO Acute Systemic Injection Test.Passed
    Pass ISO Subacute (28-Day) Subcutaneous Study.Passed
    Biocompatibility - Hemolysis (Ref. ISO 10993-4)Pass Hemolysis test.Passed
    Clinical Safety (History)Demonstrated history of safe use for glycerol-containing bone void fillers in multiple orthopedic, spinal, and dental applications.Analysis of data from clinical trials evaluating glycerol containing bone void fillers showed a history of safe use.

    2. Sample Size Used for the Test Set and Data Provenance

    • Osteoinductive Potential: The sample size for the athymic mouse assay is not specified, only that "an in vivo assay" was conducted comparing the device with at least one predicate device.
    • Biocompatibility Tests: Sample sizes for each ISO 10993 test are not specified but would follow the requirements of the individual ISO standards.
    • Data Provenance: The athymic mouse assay is an animal model. The clinical safety data is based on an analysis of existing clinical trials for "glycerol containing bone void fillers." The document does not specify the country of origin for these trials or whether the data was retrospective or prospective. It states "The Weinberg Group has analyzed data generated from clinical trials..."

    3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

    This type of information is not applicable to this submission. The ground truth for the non-clinical tests is established by the methods and endpoints defined in the respective ISO standards and laboratory protocols (e.g., histomorphometric analysis for bone formation, specific assay results for genotoxicity). There is no mention of human experts establishing "ground truth" in the way it's typically understood for diagnostic AI/ML devices.

    4. Adjudication Method for the Test Set

    Not applicable. The tests are laboratory-based and follow established protocols for objective measurement, not human expert 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/ML diagnostic device and no MRMC study was performed.

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

    Not applicable. This is a medical device (bone void filler), not a software algorithm.

    7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

    • Osteoinductive Potential: Ground truth was established by quantitative histomorphometric analysis of explanted samples from the athymic mouse assay. This is a form of direct biological measurement.
    • Biocompatibility Tests: Ground truth was established by the specific endpoints and methodologies defined by the respective ISO 10993 standards (e.g., cell viability, presence of mutations, inflammatory response).
    • Clinical Safety: Ground truth was based on the outcomes data from existing clinical trials of similar products, as analyzed by The Weinberg Group, demonstrating a history of safe use.

    8. The Sample Size for the Training Set

    Not applicable. This is not an AI/ML device, so there is no training set in the AI sense. The development of the device would involve research and development experiments, but these are not referred to as "training sets."

    9. How the Ground Truth for the Training Set Was Established

    Not applicable, as there is no training set for an AI/ML algorithm.

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