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

The Mixing and Delivery System is used to mix graft material such that it can be delivered to the patient. The system is comprised of the mixing/delivery syringe and auxiliary components including a threaded spindle with nut, open bore luer cap, pusher and cannulae which provide alternative methods of delivery for the mixed material.

This document describes the 510(k) premarket notification for the "Mixing and Delivery System" by Bone Solutions, Inc. The document is a review of the device for substantial equivalence to legally marketed predicate devices. It does not provide detailed information about clinical studies or specific acceptance criteria for AI/ML device performance.

Based on the provided text, here is an analysis of the requested information:

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

The document does not explicitly state numerical "acceptance criteria" in the context of device performance as one might expect for an AI/ML algorithm. Instead, it lists non-clinical performance testing conducted to demonstrate that the device performs as intended and met all specified criteria.

Note: The phrase "met all specified criteria" indicates that the device performed acceptably against these tests, implying these were the "acceptance criteria" for these particular tests. The document does not provide specific quantitative thresholds for these criteria.

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

This document pertains to a medical device (a mixing and delivery system for bone graft material), not an AI/ML diagnostic or prognostic algorithm. Therefore, the concepts of "test set," "sample size," and "data provenance" in the context of image data or patient data are not applicable here. The testing mentioned is engineering/performance testing of the physical device.

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)

Not applicable. This is not an AI/ML device requiring expert ground truth for a test set.

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

Not applicable. This is not an AI/ML device involving expert adjudication for a test set.

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 device, and no MRMC study is mentioned.

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

Not applicable. This is not an AI/ML algorithm.

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

Not applicable. The ground truth for the performance tests (Liquid and Air Leak Testing, Separation from Axial Load Testing, Disconnection Torsional Testing, Sterility Validation Testing and Packaging Validation Testing) would be based on established engineering standards and specifications, rather than expert consensus, pathology, or outcomes data in a clinical sense.

8. The sample size for the training set

Not applicable. This is not an AI/ML algorithm that requires a training set.

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

Not applicable. This is not an AI/ML algorithm that requires a training set or ground truth for training.

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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.

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.

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.

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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