Search Results

ULTRAPRO ADVANCED™ Mesh may be used for the repair of abdominal fascial deficiencies, such as hernias, that require the addition of a reinforcing or bridging material to obtain the desired surgical result.

ULTRAPRO ADVANCED™ Macroporous Partially Absorbable Mesh is a sterile, partially absorbable mesh device designed for the repair of hernias and other fascial defects. The implant device is composed of a macroporous mesh, manufactured out of dyed (phtalocyanine blue) and undyed non absorbable polypropylene monofilaments (3.5 mil PROLENE™) as well as a twist yarn, composed of dyed (phtalocyanine blue) and undyed non absorbable polypropylene monofilaments (3.5 mil PROLENE™) and undyed absorbable poliglecaprone 25 monofilaments (5 mil MONOCRYL™). The mesh provides blue stripes for orientation.

This document is a 510(k) premarket notification for the ULTRAPRO ADVANCED™ Macroporous Partially Absorbable Mesh. It primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study proving acceptance criteria for a novel AI/software device.

Therefore, most of the requested information for an AI/software device, such as acceptance criteria tables, sample sizes for test/training sets, expert ground truth establishment, MRMC studies, or standalone performance, is not applicable or available in this document.

However, I can extract the information relevant to the safety and performance testing that was conducted to demonstrate substantial equivalence:

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

The document does not provide a table with explicit numerical acceptance criteria and corresponding reported device performance values. Instead, it describes general categories of testing and concludes that the device "meets the requirements as defined in user specifications, performs as intended, and is substantially equivalent to the predicate device."

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

• Bench Testing: No specific sample sizes are provided for the bench top tests. The provenance is not explicitly stated, but it's implied to be within the manufacturing/testing process of the applicant/manufacturer.
• Animal Testing:
  • Pilot Study: One 28-day pilot study was performed. The sample size (number of animals) is not specified.
  • Definitive Study: One 28-day and one 91-day definitive study were performed. The sample size (number of animals) is not specified.
• Data Provenance: Not explicitly stated, but these studies would be prospective controlled animal studies.

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 studies described are animal and bench tests, not human reader studies requiring expert ground truth establishment in the context of diagnostic interpretation.

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

Not applicable, as this refers to human reader studies, which were not conducted for this device.

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 such study was conducted or is mentioned, as this is a physical medical device (surgical mesh), not an AI/software device intended to assist human readers.

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

Not applicable, as this is a physical medical device, not an algorithm.

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

For the animal studies, the "ground truth" would be established through histological analysis (tissue integration, tissue reaction) and potentially gross anatomical assessment, typically performed by veterinarians or pathologists specializing in animal studies. For bench tests, the "ground truth" is derived from measured physical properties against established engineering standards or predicate device performance.

8. The sample size for the training set

Not applicable, as this is a physical medical device and does not involve machine learning algorithms with training sets.

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

Not applicable.

Ask a specific question about this device