(28 days)
MicroMatrix® UBM Particulate is intended for the management of wounds including: partial and full thickness wounds, pressure ulcers, venous ulcers, diabetic ulcers, chronic vascular ulcers, tunnel/undermined wounds, surgical wounds (donor sites/grafts, post-Mohs surgery, post-laser surgery, podiatric, wound dehiscence), trauma wounds (abrasions, lacerations, second-degree burns, and skin tears), and draining wounds. The device is intended for one-time use.
The subject device is composed of a resorbable porcine-derived extracellular matrix (ECM) scaffolds, specifically known as urinary bladder matrix (UBM). MicroMatrix devices are supplied as a dry, absorbent, white to off-white particulate with two particle size distributions, specifically
The provided text is related to an FDA 510(k) premarket notification for a medical device called MicroMatrix® UBM Particulate. This notification is for a device (a medical product), not an AI algorithm.
Therefore, many of the requested categories related to acceptance criteria and studies for AI algorithms (such as sample size for test/training sets, data provenance, ground truth establishment, expert qualifications, MRMC studies, and standalone performance) are not applicable to this document.
The document primarily focuses on demonstrating substantial equivalence to a predicate device based on nonclinical testing (bench tests) and that no animal or clinical studies were required.
Here's a breakdown of what can be extracted from the document:
1. A table of acceptance criteria and the reported device performance
The document lists "Performance Bench Test Results" and states that for each test, the device "Meets Acceptance Criteria." It does not provide the specific numerical acceptance criteria themselves, only the conclusion that they were met.
| Test | Performance |
|---|---|
| Hydrated Onset Temperature | Meets Acceptance Criteria |
| Particle Size Analysis | Meets Acceptance Criteria |
| Package Integrity - Visual Inspection | Meets Acceptance Criteria |
| Package Integrity - Bubble Leak | Meets Acceptance Criteria |
| Package Integrity - Seal Strength | Meets Acceptance Criteria |
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample Size: Not specified for the bench tests. The document only states "All testing was performed on production equivalent devices."
- Data Provenance: Not applicable/not specified. These are laboratory bench tests on physical devices, not clinical data.
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. Ground truth for AI algorithms is not relevant for physical device bench testing. The "ground truth" here is the established test methodology and the specifications the device is designed to meet, verified by laboratory personnel.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. Adjudication methods are typically for resolving discrepancies in expert labeling or diagnoses in AI studies.
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 device is not an AI algorithm. No MRMC study was conducted.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
Not applicable. This is not an AI algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not applicable in the context of AI. For the bench tests, the "ground truth" relates to the predetermined specifications and standards that the device must meet, verified through objective measurements and observations in a laboratory setting. For example, particle size analysis would have a specified acceptable range, and the "ground truth" is whether the measured particle size falls within that range. Similarly, package integrity tests have defined parameters for success.
8. The sample size for the training set
Not applicable. This is not an AI algorithm.
9. How the ground truth for the training set was established
Not applicable. This is not an AI algorithm.
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