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DURAFIBER Ag is an effective antimicrobial dressing that is intended to provide a moist wound environment for use in the management of partial and full thickness wounds including first and second degree burns. Appropriate wounds types include:

• Chronic wounds including diabetic ulcers, leg ulcers, pressure ulcers and sores (partial & full thickness);
• surgical wounds left to heal by secondary intent;
• traumatic wounds:
• wounds that are prone to minor bleeding, such as wounds that have been mechanically or surgically debrided.

DURAFIBER Ag is a non-woven dressing made of cellulose and cellulose ethylsulphonate with silver. The product is an absorbent fibrous dressing that gels on contact with wound fluid. The silver provides the antimicrobial properties intended to reduce or inhibit microbial colonization of the device.

The silver is present in the device in the form of silver chloride. Upon contact with wound fluid, silver ions are produced from the dissociation of silver and chloride atoms. The ionic form of silver is the active antimicrobial agent.

This is a 510(k) premarket notification for the medical device DURAFIBER Ag, an antimicrobial wound dressing. The document describes the device, its intended use, and a comparison to a predicate device.

Here's an analysis of the provided text in relation to your request about acceptance criteria and supporting study information:

Description of Acceptance Criteria and Supporting Study

The provided document, a 510(k) summary, does not detail specific acceptance criteria for a new AI/software device or a formal clinical study proving its performance against such criteria. Instead, this document is focused on establishing substantial equivalence for a modified physical medical device (a wound dressing) to a previously cleared predicate device.

The "acceptance criteria" here are implicitly related to demonstrating that changes to the device (specifically, reduced silver content) do not negatively impact its safety and effectiveness compared to the predicate. The "study" in this context refers to non-clinical bench testing rather than a clinical trial with a test set, ground truth, or human readers as you would expect for an AI/software device.

Therefore, many of your specific questions are not directly applicable to this type of regulatory submission and device.

However, I will extract the information that is present and indicate where information is not applicable or not provided.

1. Table of Acceptance Criteria and Reported Device Performance

As this is a submission for a modified physical wound dressing and not an AI/software device, formal acceptance criteria in terms of metrics like sensitivity, specificity, or AUC are not specified. The "performance" is demonstrated through non-clinical bench testing to ensure the modified device remains equivalent to its predicate.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size: Not applicable. The "tests" were non-clinical bench tests (physical properties, silver release, microbiology, biocompatibility) of the device itself and not based on patient data or a "test set" in the context of an AI/software algorithm.
• Data Provenance: Not applicable. The data is from laboratory bench testing rather than 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. There was no "ground truth" derived from expert consensus for a test set, as this involved laboratory testing of a physical product, not an AI/software diagnostic tool.

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

Not applicable. There was no test set requiring expert adjudication.

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 is not an AI-assisted diagnostic device.

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

Not applicable. This is not an AI/software algorithm.

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

Not applicable. The "truth" for the non-clinical tests was determined by established laboratory measurement methods and standards relevant to the physical and antimicrobial properties of wound dressings (e.g., standard tests for material properties, silver elution, and bacterial inhibition).

8. The sample size for the training set

Not applicable. There is no training set as this is not a machine learning or AI device.

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

Not applicable. There is no training set.

Summary of the Study (Bench Testing) for DURAFIBER Ag Modification

The "study" presented here consists of non-clinical, bench-top testing to demonstrate that a modified version of the DURAFIBER Ag wound dressing (with reduced silver content) is substantially equivalent to its predicate device. This was achieved by confirming:

• The physical properties of the dressing were not altered.
• The silver release profile, while different (lower amount released), was characterized.
• The antimicrobial properties were not altered despite the reduced silver content.
• The device met biocompatibility standards (ISO 10993-1).

The conclusion is that these tests collectively demonstrate that the modified device does not raise new questions of safety and effectiveness, supporting its substantial equivalence to the previously cleared predicate.

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DURAFIBER Ag is an effective antimicrobial dressing that is intended to provide a moist wound environment for use in the management of partial and full thickness wounds including first and second degree burns. Examples of wounds types which indicated are:

• Chronic wounds including diabetic ulcers, leg ulcers, pressure ulcers and . sores (partial & full thickness);
• surgical wounds left to heal by secondary intent; .
• . traumatic wounds;
• wounds that are prone to minor bleeding, such as wounds that have been . mechanically or surgically debrided.

DURAFIBER Ag is a non woven dressing made of cellulose and cellulose ethyl sulphonate with silver. The product is an absorbent fibrous dressing that gels on contact with wound fluid. The device provides effective antimicrobial properties intended to reduce or inhibit microbial colonization of the device.
The silver is present in the device in the form of silver chloride. Upon contact with wound fluid, silver ions are produced from the dissociation of silver and chloride atoms. The ionic form of silver is the active antimicrobial agent.

The provided text describes a 510(k) summary for the DURAFIBER Ag dressing, focusing on its substantial equivalence to predicate devices rather than a direct study proving device performance against acceptance criteria. The document highlights biocompatibility testing and an animal model study, but does not present specific acceptance criteria in a quantifiable manner or a study that directly verifies those criteria with reported performance metrics.

Therefore, the following information is extracted or inferred based on the provided text. Many requested fields cannot be directly answered as the nature of this submission (substantial equivalence for a medical dressing) differs significantly from a device that would require clinical performance metrics like sensitivity, specificity, or AI assistance effect sizes.

Acceptance Criteria and Reported Device Performance

Given that this is a 510(k) submission for a wound dressing, the "acceptance criteria" are primarily related to safety, biocompatibility, and substantial equivalence to legally marketed predicate devices. The document does not specify quantitative performance metrics (like sensitivity, specificity, or error rates) that would typically be found for diagnostic or AI-driven devices.

Table of Acceptance Criteria and Reported Device Performance (Inferred from Substantial Equivalence Basis):

Study Details

The provided text describes the basis for substantial equivalence for a wound dressing, and the "studies" mentioned are primarily pre-clinical in nature for biocompatibility and an animal model. It does not describe a clinical study in humans designed to meet specific performance criteria often seen in device clearance involving diagnosis or intricate analytical tasks.

1. Sample size used for the test set and the data provenance:

• Biocompatibility Tests: The text states these were conducted "using product that has been packaged and sterilised." It does not specify a "sample size" in terms of number of samples, but rather mentions the types of tests (cytotoxicity, sensitization, irritation, subchronic toxicity, genotoxicity). The provenance of these tests is not explicitly stated (e.g., country of origin, specific lab data).
• Animal Model Study: "an animal model" was used. The specific number of animals (sample size) is not provided. The provenance (e.g., specific animal species, location of study) is also undefined.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• This information is not applicable/provided as the studies described are pre-clinical (biocompatibility, animal model) and do not involve human interpretation or expert ground truth establishment in the diagnostic sense.

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

• This information is not applicable/provided for the types of studies performed (biocompatibility, animal model).

4. 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:

• No, an MRMC study was not done. This device is a medical dressing, not an AI-assisted diagnostic or interpretation device that would involve "human readers" or "AI assistance."

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

• No, this is not applicable. The device is a physical wound dressing and does not involve an algorithm or AI.

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

• Biocompatibility: Ground truth is established by standardized, validated assays and material science principles as per ISO 10993-1:2003, with results indicating "safe for their intended use."
• Animal Model: Ground truth was based on physiological observations and comparisons of "deleterious clinical effects" against "standard treatment." This would likely involve clinical assessment of wound healing parameters in animals.

7. The sample size for the training set:

• This information is not applicable/provided. The submission describes a medical device, not an AI/ML product requiring a training set.

8. How the ground truth for the training set was established:

• This information is not applicable/provided as there is no training set for this type of device.

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