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TheraBond Antimicrobial Barrier Systems are indicated for use in light to moderately exuding partial and full thickness wounds including traumatic wounds, surgical wounds, donor sites, 1st and 2nd degree burns, as well as decubitus ulcers, diabetic ulcers and vascular ulcers. TheraBond may be used over debrided and partial thickness wounds.

The TheraBond Antimicrobial Barrier Systems consist of a knitted, flexible, silver-plated nylon-based fabric. The device is available in several sizes and configurations including wound contact dressings, island dressings and wraps.

The provided text describes a 510(k) premarket notification for the TheraBond Antimicrobial Barrier System, a wound dressing. This is a medical device submission, not an AI/ML device, therefore, much of the requested information (like sample size for test/training sets, number of experts, adjudication methods, MRMC studies, standalone performance) is not applicable or not detailed in this type of submission.

However, I can extract the acceptance criteria and the general nature of the studies performed.

Here's the information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Not Applicable. This is a physical medical device (wound dressing), not a software/AI device that uses test sets for algorithmic evaluation. The "tests" here refer to laboratory and bench testing of the physical properties and biological interactions of the device.

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

• Not Applicable. Ground truth, in the context of AI/ML, refers to annotated data. For this device, "ground truth" would be established by standardized laboratory methods and measurements against pre-defined specifications. The "experts" would be the scientists and technicians conducting the tests according to established protocols.

4. Adjudication method for the test set

• Not Applicable. Adjudication methods are typically for evaluating subjective interpretations, such as image analysis. For objective laboratory tests, results are typically compared directly against pre-defined specifications. Any discrepancies would involve re-testing or investigation into methodology, not expert adjudication in the AI/ML sense.

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

• No. This is not an AI/ML diagnostic or assistive device. MRMC studies are not relevant.

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

• Not Applicable. This is a physical medical device. There is no "algorithm only" performance to evaluate.

7. The type of ground truth used

• For antimicrobial effectiveness, silver ion release, and silver plating integrity: Standardized laboratory measurements and assays against pre-defined specifications.
• For biocompatibility: Results from standard biocompatibility tests (e.g., cytotoxicity, sensitization, irritation) in accordance with ISO 10993, compared against acceptable limits.

8. The sample size for the training set

• Not Applicable. This is a physical medical device. There is no "training set" in the AI/ML sense.

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

• Not Applicable.

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