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The Busse Surgical Drape is intended to be used as a protective patient covering, such as to isolate a site of surgical incisions from microbial and other contamination. They are provided sterile using Ethylene Oxide. This is the same intended use as a previously cleared for the Busse Surgical Drape II, K083424.

The Laminated Non-Woven Surgical drapes described in this submission are one piece, single use, designed to provide an absorbent sterile barrier & protection from microbial and other contamination. There are various sizes, with & without fenestration, and with & without adhesive strip/patch.

The provided text describes a 510(k) submission for "Busse Surgical Drapes III." The primary focus of this document is to demonstrate substantial equivalence to a predicate device, "Busse Surgical Drapes II (K083424)," not to provide a detailed study proving the device meets new, objective performance criteria in the way AI/ML medical devices often do.

Therefore, many of the requested categories related to AI/ML device studies (such as sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth types) are not applicable to this 510(k) submission for a traditional medical device (surgical drapes).

However, I can extract the relevant information regarding acceptance criteria and the "study" that demonstrates compliance based on the document's content.

Here's a breakdown:

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

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

• Sample Size: Not specified in the provided document. Biocompatibility tests typically use a small number of animal subjects or cell cultures, but the exact count is not given.
• Data Provenance: Not specified. The tests are standard biological qualification safety tests, generally performed in specialized labs. Whether these were conducted in the US or elsewhere is not stated. The data would be prospective for these specific tests on the device materials.

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)

Not applicable. This is a traditional medical device (surgical drape), not an AI/ML device requiring expert ground truth for image interpretation or diagnosis. The "ground truth" for the material tests would be the established safety thresholds and methodologies for biocompatibility, as defined by international standards (e.g., ISO 10993). The interpretation of these results is done by qualified toxicologists/biocompatibility specialists, but specific numbers and qualifications are not provided in this summary.

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

Not applicable. As this is not an AI/ML device involving human interpretation of results, there is no adjudication method in the context described.

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. This is a traditional medical device; therefore, no MRMC study, human readers, or AI assistance is involved.

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

Not applicable. This is a traditional medical device; therefore, no algorithm or "standalone" performance is relevant.

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

The "ground truth" for the reported tests is established through pre-defined scientific methods and acceptance criteria for biocompatibility testing. These criteria are based on established toxicology and material science principles, typically referenced in standards like ISO 10993. The test results (e.g., cell viability, irritation response) are compared against these predetermined thresholds.

8. The sample size for the training set

Not applicable. This is a traditional medical device; there is no "training set" in the context of an AI/ML algorithm.

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

Not applicable. As there is no training set, this question is not relevant.

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