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The ReliaTect™ Post-Op Dressing with CHG is intended to cover and protect a wound caused by percutaneous medical devices such as drains, chest tubes, orthopedic pins, fixtures, and wires.

ReliaTect™ may also be used to cover and secure a primary dressing.

ReliaTect™ inhibits microbial growth within the dressing and prevents external contamination.

The ReliaTect™ Post-Op Dressing with CHG is a transparent adhesive dressing integrated with Chlorhexidine Gluconate (CHG), a well-known antiseptic agent with broad-spectrum antimicrobial activity.

The provided document is a 510(k) summary for the ReliaTect™ Post-Op Dressing with CHG. This device is a medical dressing, not an AI/ML powered device, so information typically requested for AI/ML device studies (such as sample size for test set, data provenance, number of experts for ground truth, adjudication method, MRMC studies, standalone performance, training set details) is not applicable or available in this document.

However, I can extract information related to the acceptance criteria and performance testing that was conducted for this device.

1. Table of Acceptance Criteria and Reported Device Performance

The document states, "The acceptance criteria were met for all characteristics and comparison against the predicate demonstrated equivalent performance." It also mentions "bench testing studies, including antimicrobial efficacy, demonstrate the two devices have comparable performance."

While specific numerical acceptance criteria values are not provided, the following tests were conducted, and the device met the criteria and showed equivalent performance to the predicate device:

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 as it pertains to clinical studies often conducted for AI/ML devices. The testing mentioned (bench testing, in vitro testing, and a porcine study) are laboratory and animal studies, not human clinical trials with specific test sets in the context of AI/ML.

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 applicable and not provided. The device is a physical dressing, and its performance was assessed through laboratory and animal studies rather than expert-derived ground truth as would be used for diagnostic AI/ML systems.

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

Not applicable and not provided for the reasons stated above.

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 and not provided. This is a physical medical device, not an AI-powered diagnostic tool engaging human readers.

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

Not applicable and not provided for the reasons stated above.

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

The "ground truth" for this device's performance was established through:

• Bench Testing: Objective measurements against established physical and chemical standards for properties like fluid handling, adhesion, and moisture transmission.
• In vitro Testing: Laboratory culture results for antimicrobial efficacy, demonstrating log reduction against specific microorganisms.
• Animal Study: Direct observation and evaluation of wound healing in a porcine model, with performance compared to a predicate device.

8. The sample size for the training set

Not applicable and not provided. This is not an AI/ML device that requires a training set.

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

Not applicable and not provided for the reasons stated above.

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