(246 days)
IV Clear® is intended to cover and protect insertion sites, and secure devices to skin. Common applications include IV catheters, central venous lines, PICCs, suction catheters, epidural catheters, hemodialysis catheters, orthopedic pins, other intravascular catheters and percutaneous devices. IV Clear® inhibits microbial growth within the dressing and prevents external contamination.
IV Clear® is composed of a clear polyurethane film coated with a silicone adhesive containing chlorhexidine and silver salts.
This document describes the premarket notification (510(k)) for the IV Clear® Antimicrobial Clear Silicone Adhesive Dressing with Chlorhexidine and Silver. It does not contain information about acceptance criteria or a study that proves the device meets specific performance metrics in the format typically used for AI/ML device evaluations. This submission primarily focuses on establishing substantial equivalence to predicate devices based on safety and efficacy, rather than AI performance.
Therefore, many of the requested sections about AI performance studies, sample sizes for training/test sets, ground truth establishment, expert qualifications, and MRMC studies are not applicable to this 510(k) summary. I will answer the applicable questions based on the provided text.
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state quantitative acceptance criteria or detailed performance metrics in a tabular format as would be expected for an AI/ML device. Instead, it lists types of performance tests conducted to demonstrate substantial equivalence for the dressing.
| Acceptance Criteria Category | Reported Device Performance / Assessment |
|---|---|
| in vitro log reduction | Performed (details not provided) |
| Biocompatibility | Confirmed in accordance with ISO 10993 (cytotoxicity, sensitization, irritation, systemic toxicity, sub-chronic toxicity) |
| Porcine wound healing | Performed (details not provided) |
| Human repeat insult patch test | Performed (details not provided) |
| Human regrowth prevention | Performed (details not provided) |
| Substantial Equivalence | Confirmed to predicate devices with regard to materials, intended use, and technological characteristics. |
2. Sample sized 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 sample sizes for any test sets in the context of an AI/ML device study. It refers to a "Porcine wound healing study," a "Human repeat insult patch test," and a "Human regrowth prevention study," but no details on sample size, data provenance, or study design (retrospective/prospective) are given for these tests. These are biological/clinical tests for a medical dressing, not data sets for AI evaluation.
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 present in the document. The studies referenced are for a physical medical device (dressing), not an AI/ML diagnostic or assistive device that would require expert-established ground truth for its performance evaluation.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not applicable and not present in the document. Adjudication methods are typically used when establishing ground truth for evaluating AI/ML models, which is not the context of this 510(k) summary.
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
This information is not applicable and not present in the document. This is a 510(k) for a medical dressing, not an AI/ML device, and therefore no MRMC study or AI assistance evaluation would be conducted or described.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This information is not applicable and not present in the document. This is not an AI/ML device.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
For the studies mentioned (e.g., porcine wound healing, human repeat insult patch test, human regrowth prevention), the "ground truth" would likely be based on standard biological, clinical, or laboratory measurements and observations relevant to wound healing, skin irritation, and microbial growth inhibition. Precise details are not provided in this 510(k) summary. It would not typically involve "expert consensus" in the way it's used for image/data labeling in AI/ML.
8. The sample size for the training set
This information is not applicable and not present in the document. There is no AI/ML model for which a training set would be required.
9. How the ground truth for the training set was established
This information is not applicable and not present in the document. There is no AI/ML model for which ground truth for a training set would be established.
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