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PHMB Foam Wound Dressings are indicated for use in the temporary management (<24 hours) of post-surgical incisions, pressure sores, venous stasis ulcers, diabetic ulcers, donor sites, abrasions , lacerations, 1st and 2nd degree burns, dermatologic disorders, other wounds inflicted by trauma and, as a secondary dressing or cover dressing for packed wounds.

The subject device, PHMB Foam Wound Dressing, is a polyurethane foam impregnated with Polyhexamethylene Biguanide (PHMB), an agent that protects the dressing from bacterial penetration and colonization. The foam in the dressings has a microporous hydrophilic foam structure that absorbs wound exudate and maintains a moist wound healing environment. Based on in vitro performance data, the PHMB Foam Wound Dressing provides a barrier to bacterial penetration through the dressing and the PHMB prevents colonization and proliferation of bacteria within the dressing while in use (<24 hours). PHMB Foam dressings, when tested in-vitro have been demonstrated to be effective against the following three gram positive bacteria, three gram negative bacteria and two yeast challenge organisms within the dressing: MRSA, MRSE, VRE, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Candida albicans and Rhodotorula mucilaginosa . The device is presented in Non-border (non-adhesive) and border (adhesive) versions. The dressing is supplied sterile in a range of sizes between 4 in squared to 64 in squared .

The provided documentation is a 510(k) Summary Statement for a medical device called "PHMB Foam Wound Dressing." It describes the device, its intended use, claims substantial equivalence to a predicate device, and summarizes performance testing. However, it does not contain the specific information requested about acceptance criteria and a study proving the device meets those criteria in the context of an AI/ML device.

This document describes a conventional wound dressing, not an AI/ML powered device. Therefore, the questions related to AI/ML device performance (like sample size for test/training sets, ground truth establishment, expert adjudication, MRMC studies, standalone performance with AI, etc.) are not applicable to this document.

Here's a breakdown of what the document does provide and why it doesn't fit the AI/ML framework:

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

• Acceptance Criteria (Implied/General): The document implies acceptance criteria are met by demonstrating substantial equivalence to a predicate device and by providing in-vitro and animal testing results that show the dressing's functional and biological safety properties. These are not explicitly numerical or threshold-based acceptance criteria for an AI model's performance metrics.
• Reported Device Performance:
  • Barrier to bacterial penetration and prevention of colonization and proliferation within the dressing (<24 hours).
  • Effectiveness (in-vitro) against:
    • Three gram-positive bacteria: MRSA, MRSE, VRE
    • Three gram-negative bacteria: Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa
    • Two yeast challenge organisms: Candida albicans, Rhodotorula mucilaginosa
  • Total fluid handling.
  • Peel adhesion (for adhesive versions only).
  • Biological evaluation: Cleared as a (Category A) limited contact device (<24 hours) in accordance with BS EN ISO 10993-1.

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 for an AI/ML device. The document refers to in-vitro and animal testing, not a "test set" of data for an algorithm. Details of the sample sizes for these specific lab tests are not provided in this summary.

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

• Not applicable for an AI/ML device. "Ground truth" in this context would refer to the results of the in-vitro and animal studies, which are laboratory measurements, not expert consensus on an AI's output.

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

• Not applicable for an AI/ML device.

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 for an AI/ML device.

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

• Not applicable for an AI/ML device.

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

• Not applicable in the AI/ML sense. The "ground truth" for this device's performance would be the direct results of the microbiological and physical in-vitro tests and animal studies detailed in the summary.

8. The sample size for the training set:

• Not applicable for an AI/ML device.

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

• Not applicable for an AI/ML device.

In summary: The provided document describes a conventional medical device (wound dressing) and its regulatory submission. It does not provide any information relevant to the performance or validation of an AI/ML powered medical device.

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