Search Results

AVAGEN Wound Dressing is indicated for the management of wounds including: partial and full-thickness wounds, pressure ulcers, venous ulcers, diabetic ulcers, chronic vascular ulcers, tunneled/undermined wounds, surgical wounds (donor sites/grafts, post-Moh's 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.

AVAGEN Wound Dressing is an advanced wound dressing comprised of a porous matrix of cross-linked bovine tendon collagen and glycosaminoglycan. The biodegradable matrix provides a scaffold for cellular invasion and capillary growth.

The provided text describes a 510(k) premarket notification for the AVAGEN Wound Dressing. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than providing extensive clinical trial data with specific acceptance criteria and detailed study results from an AI perspective.

Therefore, many of the requested points, particularly those related to AI algorithm performance, sample sizes for test/training sets, ground truth establishment for AI, and MRMC studies, are not applicable to this document. The device is a physical wound dressing, not an AI-powered diagnostic or treatment device.

Here's a breakdown of the available information based on your request:

1. Table of acceptance criteria and the reported device performance

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 (N/A) / Not provided for this type of submission. The document describes a physical medical device (wound dressing) submission to the FDA via a 510(k). This process relies on demonstrating substantial equivalence to existing predicate devices, primarily through non-clinical testing (biocompatibility, physical properties). It does not typically involve clinical trials or a "test set" in the context of an AI/software device.
• The data provenance info is not relevant for biocompatibility and physical property tests which are conducted in a lab setting.

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)

• N/A. This information is not relevant for the type of device and submission described. Ground truth establishment with experts is typically associated with diagnostic devices, especially those using AI.

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

• N/A. Not relevant for the type of device and submission.

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

• N/A. This is a physical wound dressing, not an AI-assisted diagnostic device.

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

• N/A. This is a physical wound dressing, not an algorithm.

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

• N/A. For the biocompatibility and physical property tests, "ground truth" would be established by validated laboratory testing methods and results adhering to international standards (e.g., ISO standards for biocompatibility). The primary "ground truth" for the 510(k) itself is the regulatory finding of substantial equivalence based on the submitted non-clinical data and comparison to predicates.

8. The sample size for the training set

• N/A. This is a physical wound dressing, not an AI model.

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

• N/A. This is a physical wound dressing, not an AI model.

Ask a specific question about this device