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510(k) Data Aggregation
(121 days)
MicroFuse® Bone Void Filler is indicated only for bony voids or gaps that are not intrinsic to the stability of the bony structure. MicroFuse® Bone Void Filler may be combined with autogenous bone marrow aspirate or autograft. MicroFuse® Putty is a bone graft extender to be used with autogenous bone marrow aspirate and autograft. MicroFuse® is intended to be gently packed into bony voids or gaps of the skeletal system (e.g. the spine, pelvis, and extremities). These osseous defects may be surgically created or created from traumatic injury to the bone. MicroFuse® provides a bone void filler that resorbs and is replaced with bone during the healing process.
MicroFuse® Bone Void Filler is a porous bone graft scaffold composed of bonded poly (lactide-co-glycolide) or poly(lactic acid) microspheres. MicroFuse® is available with and without a combination of barium sulfate and calcium sulfate. MicroFuse® is provided in a variety of shapes and sizes, in the form of granules, sheets, pre-formed blocks, putty and ST MIS implants. MicroFuse® ST Granules and putty are designed to be gently packed into contained voids or defects. MicroFuse® Sheets are designed to be used with shallow bony defects, or as a bone graft onlay to cover a defect. MicroFuse® Blocks and ST MIS implants are designed to fill an entire defect. MicroFuse® Putty is composed of MicroFuse® ST Granules combined with an inert biodegradable carrier. MicroFuse® implants are available in short-term (ST), mid-term (MT), or long-term (LT) composition.
The provided 510(k) summary for the MicroFuse® additional implants (Putty and ST MIS) does not describe a study involving an AI/Machine Learning device or software. Therefore, many of the requested elements for an AI/ML study (e.g., sample size for test set, number of experts for ground truth, MRMC study, standalone performance, training set details) are not applicable or cannot be extracted from this document.
Instead, this submission pertains to a medical device (bone void filler) and its acceptance criteria are based on traditional medical device testing and comparison to predicate devices, rather than AI/ML performance metrics.
Here's a breakdown of the information that can be extracted, and the reasons why other information is not present:
Description of Acceptance Criteria and Device Performance (for a Medical Device, not AI/ML)
The acceptance criteria for the MicroFuse® additional implants are centered on demonstrating that the new implants are substantially equivalent to existing predicate devices and meet established performance and safety standards for their device type.
1. Table of Acceptance Criteria and Reported Device Performance:
| Acceptance Criteria Category | Reported Device Performance |
|---|---|
| Material Properties/Formulation | Meets requirements for molecular weight testing. |
| Biocompatibility | Satisfies biocompatibility testing standards. |
| Degradation Characteristics | Meets requirements for carrier degradation testing. |
| pH Stability | Satisfies pH testing. |
| Shelf Life | Meets shelf life testing standards. |
| In Vivo Performance/Safety | Satisfies animal testing requirements. |
| Special Controls Compliance | Complies with special controls provided in the "Guidance for Industry and FDA, Class II Special Controls Guidance Document: Resorbable Calcium Salt Bone Void Filler Device, Issued June 2, 2003." |
| Substantial Equivalence to Predicates | The MicroFuse® additional implants are similar to the predicate device(s) with respect to design, indications for use, and principles of operation. The information provided within this premarket notification supports substantial equivalence to the predicate device(s) (MicroFuse® Bone Void Filler K071187, K082442, and K083232). |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):
- Not applicable for an AI/ML context. This document doesn't describe a "test set" in the sense of a dataset for an algorithm. The testing described (molecular weight, biocompatibility, etc.) would involve specific samples of the device material, but the sample sizes for these specific lab tests are not detailed in this summary. Data provenance is not mentioned as it's a device manufacturing and performance claim, not a data-driven diagnostic.
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 for an AI/ML context. No "ground truth" establishment by human experts is mentioned for this device submission, as it's not an AI diagnostic or image analysis tool.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- Not applicable for an AI/ML context. This concept is irrelevant for the type of device and testing described.
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 pertains to AI-assisted diagnostic tools, which this device is not.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Not applicable. This device is a physical bone void filler, not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
- Not applicable in the AI/ML sense. For this device, "ground truth" would relate to accepted scientific methods for evaluating material properties, biological response (e.g., animal models), and adherence to established regulatory standards for bone void fillers. The ground truth for its performance is derived from these standardized tests, not human expert interpretation of data.
8. The sample size for the training set:
- Not applicable. This summary does not describe an AI/ML device with a "training set."
9. How the ground truth for the training set was established:
- Not applicable. This summary does not describe an AI/ML device.
In summary: The provided document is a 510(k) summary for a physical medical device (bone void filler) and its substantial equivalence to predicate devices, based on standard engineering, chemical, and biological testing, rather than an AI/Machine Learning diagnostic or assistive software. Therefore, the questions formulated for an AI/ML device are largely inapplicable.
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