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510(k) Data Aggregation

    K Number
    K190996
    Date Cleared
    2019-07-28

    (103 days)

    Product Code
    Regulation Number
    880.5860
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    Bonus Therapeutics Mixing and Delivery System

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

    The Bonus Therapeutics Mixing and Delivery System is intended to be used for the delivery of hydrated allograft, autograft, or synthetic bone graft material to surgical sites.

    Device Description

    The Bonus Therapeutics Mixing and Delivery System comprises a sterile piston syringe with an end cap, a cannulated applicator tip and a pusher rod. The syringe consists of syringe barrel with a plunger and an integrated mixing rod to enable mixing of the grafting material prior to application. The open bore barrel enables the loading of viscous grafting material by transferring it directly into the sterile syringe. The plunger sleeve is removable, and when removed it exposes the integrated mixing rod; a rotatable rod with four diagonal blades which allow mixing of the material as needed prior to injection, while maintained in the closed syringe barrel. When the plunger sleeve is attached, it operates as a simple piston to allow the extrusion of the material from the syringe. The Cannula may be attached to the syringe via the Luer connection to facilitate the delivery of grafting material to the surgical site. The Pusher may be used to release grafting material remaining in the Cannula. The system is supplied sterile for single use.

    AI/ML Overview

    The provided FDA 510(k) summary (K190996) for the Bonus Therapeutics Mixing and Delivery System focuses on demonstrating substantial equivalence to a predicate device, rather than proving performance against specific acceptance criteria for an AI/ML powered medical device. Therefore, the information required to directly answer some of the questions, particularly those related to AI/ML device performance (like expert readers, ground truth, MRMC studies, standalone performance), is not present in this document.

    However, I can extract the information related to the device's functional performance where acceptance criteria would implicitly be expected, and describe the type of studies conducted.

    Here's a breakdown of the available information:

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

    The document does not explicitly state acceptance criteria in a quantitative table format alongside performance results. Instead, it lists the types of tests performed to assure the device performs as intended and states that the device design was "qualified" and that tests were performed "to assure that the device performs as intended." This implies that the device met internal specifications for each test.

    Test CategoryImplied Acceptance Criteria (Based on typical device requirements)Reported Device Performance (as implied by document)
    BiocompatibilityNo adverse biological reactionsConducted studies for Cytotoxicity, Sensitization, Irritation, Acute Systemic Toxicity, Material-mediated Pyrogenicity in accordance with ISO 10993-1. (Implies satisfactory results)
    SterilitySterility Assurance Level (SAL) of 10^-6Sterilization process validated according to AAMI ISO TIR 13004:2013 using the VDmax method. (Implies SAL met)
    Shelf LifeMaintain sterile barrier & device performance for 3 yearsValidated according to ISO 11607-1, including package integrity (visual inspection, peel test, dye penetration) post-sterilization & aging, and device performance post-aging. (Implies 3-year shelf life met)
    Mechanical PerformanceMaintain functionality, no leaks, proper friction, cap closure, prevent clogging, adequate mixing rod torquePerformed numerous tests on new and aged devices: Syringe tightness, Piston friction, Performance limit - cap closure, Performance limit - clogged cannula attachment, Mixing rod torque resistance. (Implies satisfactory performance for these features)

    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 does not explicitly state the sample sizes for the mechanical and performance bench testing. It generically mentions "test units representative of the finished devices" and "devices at different time points of their shelf life."

    For biocompatibility, sterility, and shelf-life, specific sample sizes are not provided, but these studies are typically conducted with a defined number of samples according to the relevant international standards (e.g., ISO, ASTM, AAMI).

    Data provenance (country of origin, retrospective/prospective) is not mentioned, as these are typically in-house engineering and lab studies, not clinical data sets.

    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 question is not applicable. The device is a physical medical device (mixing and delivery system), not an AI/ML powered device requiring expert-established ground truth for image interpretation or diagnosis. The "ground truth" for this device's performance relates to its physical and biological properties meeting engineering specifications and safety standards.

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

    This question is not applicable for the type of device described. Adjudication methods like 2+1 or 3+1 are used for clinical image interpretation studies where human expert consensus is needed to establish ground truth for AI algorithms. The studies mentioned here are bench tests, sterilization validations, and biocompatibility evaluations, which have objective endpoints or established laboratory methods.

    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 question is not applicable. The device is not an AI-powered diagnostic or assistive tool, so an MRMC study comparing human readers with and without AI assistance is not relevant.

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

    This question is not applicable. The device is a physical medical device, not an algorithm.

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

    For this device, the "ground truth" is defined by established engineering specifications, international standards (e.g., ISO, ASTM, AAMI), and regulatory requirements. For example:

    • Biocompatibility: Absence of toxic, sensitizing, or irritating effects as per ISO 10993.
    • Sterility: A SAL of 10^-6, verified by validated sterilization methods.
    • Mechanical Performance: Meeting pre-defined tolerance ranges for parameters like leak tightness, piston friction, and torque resistance.
    • Shelf Life: Maintaining package integrity and functional performance within specifications over the specified duration.

    8. The sample size for the training set

    This question is not applicable. There is no AI/ML algorithm involved, and thus no "training set" in the context of machine learning. The term "training set" might loosely refer to the historical data or knowledge used to design the device and establish its specifications, but it's not a formal concept in this context.

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

    This question is not applicable, as there is no AI/ML algorithm or "training set" in the relevant sense. The design and specifications of the device are based on established engineering principles, material science, user needs, and regulatory standards for medical devices of this class.

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