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The Acticoat® Moisture Control dressing, an addition to the Acticoat® Dressing line of products is intended as an absorbent dressing which provides an effective barrier to bacterial penetration. Acticoat® Moisture Control Dressing is indicated for use in light to moderately exuding partial and full thickness wounds including decubitus ulcers, diabetic ulcers, 1st and 2nd degree burns, and donor sites. Acticoat® Moisture control may be used over debrided and partial thickness wounds.

The absorbent, antimicrobial barrier dressing consists of three layers. An outer blue polyurethane film layer, a central polyurethane foam layer and a nanocrystalline silver coated polyurethane film wound contact layer. The layers are heat laminated together to form a single dressing. The dressing can be cut to size and maintains its antimicrobial activity for up to 7 days.

This 510(k) premarket notification for K050030, the Acticoat® Moisture Control Dressing, is for a wound dressing and as such, does not involve the typical software-driven device acceptance criteria and studies you might expect for an AI/ML product. The submission is focused on demonstrating substantial equivalence to a predicate device, which means proving that the new device is as safe and effective as a legally marketed device.

Therefore, the requested information about acceptance criteria, device performance, sample sizes for test sets, data provenance, expert ground truth, adjudication methods, MRMC studies, standalone performance, training sets, and ground truth establishment for AI/ML models is not applicable to this type of device submission.

Instead, the "acceptance criteria" for this submission are met by demonstrating the following:

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

Regarding the specific questions which are NOT applicable to this 510(k) submission for a wound dressing:

2. Sample sizes used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable for a wound dressing substantial equivalence submission. Performance data for a direct animal or human study for this specific claim of equivalence is not detailed in the summary.
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" in the context of AI/ML models is not relevant here.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
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/imaging device.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable. This is not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable. The "ground truth" here is the established safety and effectiveness of the legally marketed predicate device.
8. The sample size for the training set: Not applicable. There is no AI/ML model for which a training set would be used.
9. How the ground truth for the training set was established: Not applicable.

In summary, this 510(k) document demonstrates substantial equivalence by comparing the new device's intended use, technological characteristics (materials, design, manufacturing), and biocompatibility to a predicate device (Acticoat® Foam Dressing K000051) which has already been deemed safe and effective. The FDA's acceptance of this submission means they agreed that the Acticoat® Moisture Control Dressing is substantially equivalent to the predicate and does not raise new questions of safety or effectiveness.

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Avitar Hydrasorb Sterile Dressings are external wound dressings designed to provide a moiest healing environment, manage exudate, and protect the wound from contamination. Hydrasorb Sterile Dressings are indicated as external wound dressings for use in the local management of external wounds such as post surgical incisions, pressure sores, venous stasis ulcers, diabetic ulcers, donor sites, abrasions, lacerations, superficial burns, other external wounds inflicted by trauma, and as a secondary dressing or cover dressing for packed wounds. Hydrasorb Sterile Dressing -Fenestrated also absorbs fluid quickly and efficiently and maintains a moist wound healing environment. is an ideal dressing in the local management of exudate that may occur at surgically induced body drainage sites such as a tracheostomy, G-tube, J-tube, Penrose drain, chest tube, nephrotomy tube, or sump In generallit can be used anywhere there is a need to manage drain. the surrounding skin due to potential skin irritation or maceration.

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The provided document is a 510(k) premarket notification decision letter from the FDA regarding the Avitar Technologies, Inc. Hydrasorb™ Sterile Dressings. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving the device meets specific acceptance criteria through a clinical study with detailed performance metrics.

Therefore, much of the requested information (acceptance criteria, specific study details, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth establishment) is not typically part of a 510(k) submission and is not present in the provided text.

The document states that the Avitar Hydrasorb™ Sterile Dressings are "comparable in their effectiveness" to several predicate devices. This statement of comparability constitutes the "acceptance criteria" in the context of a 510(k) – that the new device performs as well as, or no worse than, an existing device.

Here's a breakdown of what can be extracted from the document:

1. A 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)

This information is not provided in the document. A 510(k) submission primarily relies on non-clinical data and comparison to predicate devices, and often does not include detailed clinical study data with specific test set sample sizes.

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)

This information is not provided in the document. The concept of "ground truth" established by experts for a test set is typically associated with clinical studies, especially those involving diagnostic devices or AI, which are not the focus of this 510(k) for a wound dressing.

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

This information is not provided in the document. Adjudication methods are relevant to clinical studies involving human interpretation or assessment, which is not described 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

An MRMC study is not mentioned and is not applicable to this type of device (wound dressing) or regulatory submission (510(k)). This type of study is usually for AI-powered diagnostic tools.

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

A "standalone" algorithm performance is not applicable to this device. This term refers to the performance of artificial intelligence algorithms, which are not involved in this wound dressing.

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

The document primarily relies on the physical and functional characteristics of the device and comparison to predicate devices. There is no explicit mention of a "ground truth" in the context of expert consensus, pathology, or outcomes data for a specific test set within this 510(k) summary. The "ground truth" for "effectiveness" is implicitly defined by the established performance of the predicate devices.

8. The sample size for the training set

This information is not provided and is not applicable. The concept of a "training set" (in the context of machine learning or AI) is not relevant to this traditional medical device.

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

This information is not provided and is not applicable for the same reasons as point 8.

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