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BonVie+ is an implant intended to fill bony voids or gaps of the skeletal system (i.e., the extremities and pelvis). These osseous defects are surgically created or the result of traumatic injury to the bone and are not intrinsic to the stability of the bony structure. BonVie+ resorbs and is replaced with bone during the healing process.

BonVie+ is an osteo-conductive bone void filler. It is supplied sterile in a kit comprised of 1] a pack containing polymers and Ca-salt in powdered form, 2] ampoules containing a biocompatible solution (acetone) for mixing and granulation, and 3] a silicone mat containing cavities of various sizes to form granules of various sizes. The powder formulation comprises hydroxyapatite (HA), calcium carbonate (CaCO3), and calcium chloride (CaCl2) particles with degradable polymers poly(caprolactone) (PCL), poly(ethylene qlycol) (PEG), and poly(lactide-co-dlycolide) (PLGA). The biocompatible solution used for mixing is acetone. Once mixed and granulated, the HA and CaCO3 particles are dispersed throughout the entire structure of the device. Upon implantation, the HA and CaCO3 particles resorb over time. The polymer-based binding matrix also resorb with time. When BonVie+ is placed in direct contact with viable bone and the surrounding biologic fluid environment, porous regions form in the filler material and are infiltrated with bone tissue. Bone formation occurs in apposition to the HA and CaCO3 surface and within the pores of the device resorbs, bone grows into the space previously occupied by the filler material.

This document is a 510(k) summary for a medical device called BonVie+, a resorbable calcium salt bone void filler. The information provided outlines the device, its intended use, and a summary of non-clinical tests to demonstrate substantial equivalence to a predicate device, EP Granules™ BVF.

Based on the provided text, the device itself (BonVie+) is a bone void filler and not an AI/Software device. Therefore, the acceptance criteria and study information typically associated with AI/ML-based medical devices (like those involving test sets, training sets, expert ground truth, MRMC studies, etc.) are not present in this document.

The document describes the device's physical and chemical properties, manufacturing, and preclinical testing to show equivalence to a predicate. It does not mention any AI or software components that would require the kind of performance evaluation requested (e.g., diagnostic accuracy metrics like sensitivity, specificity, AUC).

Therefore, I cannot provide the requested information regarding acceptance criteria, study details, sample sizes, expert involvement, or MRMC studies, as the provided text does not describe a study involving an AI/Machine Learning component.

To directly answer your prompt based on the provided text:

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

• Acceptance Criteria: Not explicitly stated in terms of quantitative performance metrics for an AI/Software device. The FDA's acceptance is based on "substantial equivalence" to a predicate device, demonstrated through non-clinical tests.
• Reported Device Performance:
  • In-vitro Degradation and Surface Characterizations of BonVie+
  • X-ray Diffraction (XRD) of BonVie+
  • Residual Solution in BonVie+ granules
  • Usability Testing
  • Compression testing
  • Sterilization and packaging validation
  • Biocompatibility testing in accordance with ISO 10993
  • Pyrogenicity testing/endotoxin monitoring.
  • Note: Specific numerical results or statistical outcomes from these tests are not provided in this summary. The summary only lists the types of tests performed to support substantial equivalence.

2. Sample sizes used for the test set and the data provenance:

• Not applicable as this is not a study of an AI/Software device performance on a test set of data. The tests listed are for material properties and device safety.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable as no "test set" or "ground truth" (in the context of AI/Software performance) is mentioned for this medical device.

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

• Not applicable.

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:

• No MRMC study was mentioned. This device does not appear to involve human readers or AI assistance in interpretation.

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

• Not applicable.

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

• Not applicable. The "ground truth" for this device's acceptance is its compliance with material properties, sterility, biocompatibility, and mechanical performance demonstrated through standard testing, not diagnostic accuracy against a clinical ground truth.

8. The sample size for the training set:

• Not applicable.

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

• Not applicable.

In summary, the provided FDA 510(k) letter and summary describe a traditional medical device (bone void filler) and its non-clinical testing for substantial equivalence, not an AI/ML-driven device. Therefore, the specific criteria and study methodologies you've asked about, which are pertinent to AI/ML device evaluations, are not present in this document.

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