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Fluoro-Tex154 Surgical Membrane is an implant material which is intended to be used for the repair or replacement of the peritoneum and pericardium.

The Bridger Biomed Fluoro-Tex™ Surgical Membrane is substantially equivalent to Gore-Tex® Surgical Membrane. Both devices consist of a sheet of porous expanded polytetrafluoroethylene (ePTFE), approximately 0.1 millimeters thick. The Fluoro-TexTM Surgical Membrane and the predicate device are intended for the repair or replacement of the peritoneum.

This document is a 510(k) premarket notification for a medical device, the Fluoro-Tex™ Surgical Membrane. The primary focus of a 510(k) is to demonstrate substantial equivalence to a predicate device, not necessarily to establish new acceptance criteria or conduct extensive studies demonstrating de novo performance.

Therefore, the requested information focuses on aspects that are not typically the subject of a traditional 510(k) submission for a device like a surgical membrane. Many of the fields you've requested (e.g., sample size for test sets, number of experts, adjudication methods, MRMC studies, standalone algorithm performance, training set details) are relevant to the evaluation of AI/ML-driven devices or diagnostic tools, which is not what this document describes.

Here's an analysis 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 (e.g. country of origin of the data, retrospective or prospective):

• The document describes non-clinical mechanical and chemical tests, not a clinical study with a "test set" in the context of, for example, diagnostic imaging.
• Therefore, specific sample sizes for a patient-derived test set are not applicable and not reported.
• Data provenance (country, retrospective/prospective) is not applicable as there were no patient data sets for testing.

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 expert consensus on medical images or diagnoses is not relevant for this type of device (a surgical membrane). The "ground truth" here would be the physical and chemical properties of the materials themselves, evaluated through standard engineering and materials science methods.

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

• Not applicable. Adjudication methods are used to resolve disagreements among human readers/experts, which is not relevant for the non-clinical testing 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:

• No. This is not an AI/ML-driven device or a diagnostic tool that would typically undergo an MRMC study.

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

• No. This is not an algorithm; it's a physical medical device.

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

• The "ground truth" for the device's performance was established through non-clinical mechanical and chemical tests, comparing its inherent material properties and performance characteristics (e.g., strength, porosity, material identification) directly against those of the predicate device.

8. The sample size for the training set:

• Not applicable. This device does not use a "training set" in the context of AI/ML or statistical modeling.

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

• Not applicable. See point 8.

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