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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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