(84 days)
Porcine Dermal Xenograft is intended for the management of wounds that include: Partial and full thickness wounds; Pressure, diabetic, and venous ulcers; Chronic vascular ulcers; Second-degree burns; Surgical wounds-donor sites/grafts, post-Mohs surgery, post-laser surgery, podiatric, wound dehiscence; Trauma wounds-abrasions, lacerations, and skin tears; Tunneled/undermined wounds; Draining wounds. The Porcine Dermal Xenograft provides an environment that supports wound healing and control of minor bleeding. The device is intended for single patient, one time use only.
Porcine Dermal Xenograft is an acellular, sterile, porcine dermal xenograft for use in treatment of topical wounds. The product is available in several sizes.
Here's an analysis of the provided 510(k) summary, specifically addressing the acceptance criteria and study proving device performance, although it's crucial to note that this document describes a medical device (a xenograft) and NOT an AI/ML powered device. Therefore, many of the typical questions for AI/ML device studies (like sample size for training, expert ground truth, MRMC studies, etc.) are not applicable to this submission.
The provided document, K113866, is a 510(k) summary for a Porcine Dermal Xenograft. This is a traditional medical device (a biological product derived from animal tissue for wound care), not an AI/ML powered device. As such, the "acceptance criteria" and "study" described are focused on bench testing and biocompatibility to demonstrate substantial equivalence to predicate devices, rather than performance metrics like sensitivity, specificity, or reader improvement, which are common for AI/ML.
Let's address the points as best as possible given the nature of the device:
Acceptance Criteria and Device Performance (for a Porcine Dermal Xenograft)
Given that this is a Porcine Dermal Xenograft and not an AI/ML-powered device, the concept of "acceptance criteria" is tied to its material properties, biocompatibility, and functionality for wound management, as demonstrated through bench testing and comparison to predicate devices. There are no performance metrics like sensitivity, specificity, or AUC as would be seen for an AI/ML device.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Bench Test/Biocompatibility) | Reported Device Performance |
|---|---|
| Technological Characteristics: | Identical to predicate devices (Porcine Surgical Mesh, PriMatrix, and LTM Wound Dressing). |
| Biocompatibility (ISO 10993-1): | "The product met all of the stated requirements of each test." This implies successful completion of tests for cytotoxicity, sensitization, irritation, acute systemic toxicity, etc., as per ISO 10993-1 standards for medical devices. Specific numerical results are not provided in this summary. |
| Safety and Effectiveness for Intended Use: | "Bench testing has demonstrated that the device is safe and effective for its intended use..." |
| Substantial Equivalence: | "...and that its performance is substantially equivalent to the predicate devices." |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: Not explicitly stated in terms of a "test set" for performance metrics typical of AI models. The "sample" would refer to the number of xenograft units used for bench testing (e.g., tensile strength, hydration properties, porosity) and biocompatibility testing. This kind of detail is usually found in the full 510(k) submission, not the summary.
- Data Provenance: Not applicable in the context of clinical data for AI/ML. The "data" here comes from laboratory bench tests and in vitro or in vivo biocompatibility studies conducted by or for Brennen Medical. The country of origin for such data would typically be the location of the testing laboratories. The study is not retrospective or prospective in the sense of a clinical trial used for AI/ML validation; it refers to device specific analytical and biocompatibility testing.
3. Number of Experts Used to Establish Ground Truth and Qualifications
- Not Applicable. This is not an AI/ML device where expert consensus is used to label medical images or clinical outcomes. Ground truth for a xenograft involves laboratory standards (e.g., specified ranges for material properties, pass/fail for biocompatibility tests). The "experts" would be the scientists and engineers conducting the tests and interpreting the results against established standards.
4. Adjudication Method for the Test Set
- Not Applicable. There is no "adjudication" of expert opinions as would be needed for an AI/ML test set. The results of the bench tests and biocompatibility tests are empirical measurements and observations against pre-defined acceptance criteria for those tests.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
- No. An MRMC study is relevant for AI/ML devices that assist human readers in tasks like image interpretation. This device is a physical wound dressing and does not involve human "readers" or image interpretation. Its "effectiveness" is demonstrated through its physical and biological properties for wound management, and comparison to predicate devices.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done
- Not Applicable. This is not an algorithm. The device "performance" is its intrinsic physical and biological characteristics.
7. The Type of Ground Truth Used
- Engineering and Biological Standards / Predicate Device Equivalence. The "ground truth" for this device's performance is established by:
- Validated laboratory test methods: Meeting predefined specifications for physical properties (e.g., strength, porosity, sterility) through bench testing.
- ISO 10993-1 Biocompatibility Standards: Pass/fail criteria for biological safety tests.
- Comparison to predicate devices: Demonstration that the new device's characteristics and performance are "identical" or "substantially equivalent" to legally marketed predicate devices, which have already demonstrated safety and effectiveness for similar intended uses.
8. The Sample Size for the Training Set
- Not Applicable. This is not an AI/ML device and therefore does not have a "training set."
9. How the Ground Truth for the Training Set Was Established
- Not Applicable. As there is no training set for this type of device, no ground truth needs to be established for it.
In summary, the provided document is a 510(k) summary for a traditional medical device (a xenograft). The "acceptance criteria" and "study" proving its performance are based on bench testing, biocompatibility testing against international standards (ISO 10993-1), and demonstrating substantial equivalence to already cleared predicate devices, rather than the AI/ML-specific performance metrics and study designs (like MRMC, training/test sets, expert ground truth) that your questions imply.
N/A