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Biatain Ag Foam Adhesive & Non-Adhesive Dressings are indicated for use in the management of moderately to highly exuding leg ulcers and pressure sores. The dressing can also be used for 2nd degree burns, donor sites, post operative wounds and skin abrasions. Biatain Ag Foam Non-Adhesive Dressings are additionally indicated for diabetic foot ulcers. Biatain Ag Foam Cavity Dressings are indicated for deep wounds with moderate to high amounts of exudate. Biatain Ag Foam Cavity Dressings are indicated for stage II, III and IV pressure ulcers, leg ulcers, diabetic foot ulcers and first or second degree burns with significant loss of tissue.

Biatain Ag Foam Dressings are wound dressings for exuding wounds with delayed healing due to bacteria, or where there is a risk of infection. Biatain Ag Foam Dressings consist of soft, absorbing polyurethane (PU) foam pads with a moisture-permeable PU topfilm on one side and a smooth, wound-contact surface on the other side. The foam is directly on the topfilm, except for Biatain Ag Cavity which has no topfilm. The foams contain silver, which is released upon contact with wound exudate. The dressings are available in different sizes and shapes, including squares and rectangles, with rounded corners. Biatain Ag Non-Adhesive Foam Dressings also have bevelled edges. Foam thicknesses are 3 mm or 4.4 mm. The dressings are individually-packed in a pouch. All dressings are sterile and are for single use only.

The provided text describes a 510(k) summary for Biatain Ag Foam Dressings and indicates that substantial equivalency is supported by bench testing and biocompatibility testing. It does not contain information about acceptance criteria or a study proving the device meets specific acceptance criteria in the context of device performance metrics such as sensitivity, specificity, or reader studies.

The document is a regulatory submission for a medical device (wound dressing) and its focus is on demonstrating substantial equivalence to a predicate device, as required for 510(k) clearance by the FDA. Such submissions typically do not include detailed performance studies with acceptance criteria in the way an AI/software device would.

Therefore, most of the requested information cannot be extracted from the given text.

Here's what can be extracted based on the document:

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

   • Not applicable / Information not provided. The document states "Substantial equivalency is supported by bench testing compared to the predicate device and biocompatibility testing performed on the subject device." It does not present specific acceptance criteria or performance metrics (like sensitivity, specificity, agreement rates) in a tabular format typically seen for algorithm performance. The "performance" here refers to the overall product functionality and safety, not AI-driven diagnostic accuracy.

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 / Information not provided. This document does not describe a clinical performance study with a test set. The "bench testing" mentioned generally involves laboratory tests, not patient data in the sense of a clinical trial for AI performance.

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 / Information not provided. This information pertains to studies where ground truth needs to be established through expert review, typically for AI or diagnostic devices. This document describes a physical wound dressing, not a diagnostic tool requiring expert ground truth for performance evaluation.

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

   • Not applicable / Information not provided. As there's no clinical performance test set or expert ground truth process described, adjudication methods are not relevant here.

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 / Information not provided. This document describes a physical medical device (wound dressing), not an AI-assisted diagnostic tool. Therefore, an MRMC study with human readers and AI assistance is not relevant to this submission.

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

   • Not applicable / Information not provided. The device is a physical wound dressing, not an algorithm.

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

   • Not applicable / Information not provided. For "bench testing" and "biocompatibility testing," ground truth refers to established laboratory standards and test methodologies, not clinical ground truth like pathology or expert consensus for AI performance.

8. The sample size for the training set

   • Not applicable / Information not provided. This document is not about an AI device, so there is no concept of a "training set."

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

   • Not applicable / Information not provided. As above, there is no AI training set.

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