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The VivoSorb Sheet is indicated for the use as a temporary wound support, to reinforce soft tissues where weakness exists, or for the repair of hernia or other fascial defects that require the addition of a reinforcing material to obtain the desired surgical result. The resorbable protective film minimizes tissue attachment to the device in case of direct contact with the viscera.

The VivoSorb Sheet is designed to be a flexible and transparent resorbable Device poly (DL-lactide-co-s-caprolactone) sheet to provide support to soft tissue Description where weakness exists. The VivoSorb Sheet is provided sterile in Tyvek pouch packages in a variety of sizes.

This document is a 510(k) summary for the VivoSorb Sheet, a medical device. It does not describe an AI/ML powered device, therefore the standard acceptance criteria for such a device are not applicable.

Here's an analysis of the provided information, focusing on the device's performance claims and the study used to support them, based on the document's content:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly define "acceptance criteria" in a quantitative manner as would be expected for an AI system, nor does it present specific numerical performance targets. Instead, it relies on demonstrating substantial equivalence to predicate devices through various tests.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: The document does not specify the sample sizes used for the "design verification tests" or "animal tests." It only lists the types of tests performed (e.g., in vitro suture retention, degradation, aging, mechanical, biocompatibility, animal tests).
• Data Provenance: The document does not provide details on the country of origin of the data or whether the studies were retrospective or prospective. It only mentions "design verification tests and analyses" and "animal tests."

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

This information is not applicable to this document as it describes a physical medical device and its performance, not an AI/ML system that requires ground truth established by experts. The "ground truth" here would be the physical properties and biological responses observed in the tests.

4. Adjudication Method for the Test Set

This information is not applicable for the same reason as point 3. No expert adjudication method is described or required for the device's physical and biological testing.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

This information is not applicable. The device is a physical surgical mesh, not an AI system intended to assist human readers. Therefore, an MRMC study is not relevant.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

This information is not applicable. The device is a physical surgical mesh, not an algorithm.

7. The Type of Ground Truth Used

For this physical device, the "ground truth" was established through:

• Objective Measurement/Pathology: This would include direct measurements from the "in vitro suture retention testing," "in vitro degradation testing," "aging testing," and "mechanical testing." For "biocompatibility testing" and "animal tests," "pathology" (histological analysis, observation of biological responses) would be used to assess safety and interaction with biological tissues.
• Literature and Predicate Device Comparison: The determination of "substantial equivalence" also relies on the established performance and safety profiles of the predicate devices and existing scientific literature.

8. The Sample Size for the Training Set

This information is not applicable. This is a physical device, not an AI/ML model that requires a training set.

9. How the Ground Truth for the Training Set Was Established

This information is not applicable. As it's not an AI/ML model, there is no "training set" and associated ground truth in that context. The "ground truth" for the device's development would be based on engineering principles, material science, and biological understanding, informed by pre-clinical testing mentioned in point 7.

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