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For the topical management of minor cuts, lacerations, abrasions, 1st and 2nd degree burns, and skin irritations.

ASAP Wound Dressing Gel is an amorphous, water-based gel that contains silver hydrosol that may inhibit the growth of microorganisms within the dressing. The high moisture content gel contains a base matrix composed of hydrophilic and buffering compounds and contains silver from American Biotech Labs' proprietary silver hydrosol suspension. ASAP Wound Dressing Gel is supplied in a multi-dose gel pump and a tube (collapsible, low-density polyethylene tube, sealed on one end and fitted with a pop-open screw cap on the other end).

This document describes a 510(k) submission for the ASAP Wound Dressing Gel, a silver hydrosol-based wound dressing. The submission asserts substantial equivalence to predicate devices and does not contain details about acceptance criteria, a specific study proving device performance against such criteria, or the methodology typically used in AI/ML medical device studies (like sample sizes for test/training sets, ground truth establishment, MRMC studies, or standalone performance).

Here's an analysis based on the provided text, focusing on the questions that can be answered:

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

The document does not explicitly define acceptance criteria as pass/fail metrics for specific performance endpoints (e.g., sensitivity, specificity, accuracy). Instead, it states that the device was evaluated through standard biological reactivity tests (ISO 10993) and antimicrobial effectiveness testing in accordance with USP . The acceptance criterion essentially appears to be "found to be acceptable" for biological reactivity and "effectiveness was established" for antimicrobial properties.

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

The document does not provide details on the sample size for any test set or the provenance of data. The "testing" mentioned refers to laboratory standards (ISO, USP) rather than clinical or image-based studies with patient 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)

This is not applicable as the document describes a wound dressing and not an AI/ML-driven diagnostic device that would typically rely on expert consensus for ground truth. The "ground truth" here would be defined by the results of the specific biological and antimicrobial assays.

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

This is not applicable for the same reasons as point 3. The document does not describe a scenario requiring expert adjudication of results.

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 was not conducted (and would not be relevant) as this is not an AI-assisted diagnostic device.

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

This question is not applicable as the device is a wound dressing, not an algorithm.

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

The "ground truth" for the device's claims are based on:

• Biological Reactivity: Results from ISO 10993 standard tests. This would involve laboratory measurements against established biological endpoints (e.g., cytotoxicity, sensitization).
• Antimicrobial Effectiveness: Results from USP testing. This involves standardized microbiological assays to determine the product's ability to inhibit microbial growth.

8. The sample size for the training set

This question is not applicable as the device is a wound dressing and does not involve AI/ML models requiring a training set.

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

This question is not applicable for the same reason as point 8.

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