(155 days)
Atteris Antimicrobial Barrier Film Dressing is intended for application to minor wounds and damaged skin as a liquid, film forming barrier, which creates a waterproof, film dressing, protecting the wound or damaged skin.
Atteris™ Antimicrobial Barrier Film Dressing is a polymeric solution which forms a uniform film when applied to minor wounds and damaged skin. The product is dispersed in a unique non-cytotoxic, non-stinging solution via a standard 28 mL pump spray bottle. The product is biocompatible, non-stinging, fast drying, and has low friction. Antimicrobial Barrier Film Dressing protects minor wounds and damaged skin by providing a secure, breathable, waterproof barrier to external contaminates. The film dressing is colorless, transparent, and possesses good oxygen and moisture vapor permeability. The antimicrobial PHMB at a concentration of 0.001% w/w is added to the product as a preservative to inhibit the growth of microorganisms within the product.
The provided document is a 510(k) premarket notification for the "Atteris Antimicrobial Barrier Film Dressing" and focuses on demonstrating substantial equivalence to a predicate device, rather than presenting a study to prove a device meets specific acceptance criteria in the context of AI/machine learning performance.
Therefore, most of the requested information regarding AI/ML study design (sample size for test/training sets, data provenance, expert ground truth, adjudication methods, MRMC studies, standalone performance) is not applicable to this document.
However, I can extract the acceptance criteria and reported device performance from the provided "Performance Testing" section.
Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Preservative Effectiveness (USP 51) | Broad spectrum activity demonstrated against 5 organisms (Gram positive, Gram negative, and fungal types). Total kill was achieved for 24 hours at >6 log of each organism: P. aeruginosa, E. coli, C. albicans, S. aureus, A. brasiliensis. Performance was sustained initially and at several points during aging. This indicates the product is preserved using PHMB. |
| Biocompatibility (ISO 10993 parts 1, 5, 10) | The device is non-cytotoxic, non-sensitizing, and non-irritating. This was demonstrated through cytotoxicity, sensitization, and irritation studies. |
| Shelf Life Stability | Real-time aging studies indicate the product is expected to be stable and effective for a shelf life of 6 months. |
Answers to other questions based on the provided document:
- Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable. The document describes in-vitro lab testing, not a clinical study with a "test set" of patients/data.
- Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.
- Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
- 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/ML device.
- If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an AI/ML device.
- The type of ground truth used (expert consensus, pathology, outcomes data, etc): The "ground truth" or reference standards for the performance tests were established using recognized international standards and methods, specifically:
- USP 51 for antimicrobial effectiveness.
- ISO 10993 parts 1, 5, and 10 for biocompatibility.
- The sample size for the training set: Not applicable. This is not an AI/ML device.
- How the ground truth for the training set was established: Not applicable.
In summary, this document is a regulatory submission for a medical device (a film dressing) and its performance is evaluated through standard laboratory and biocompatibility testing, not through AI/ML performance studies.
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