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The ReGen Collagen Scaffold (CS) is intended for use in surgical procedures for the reinforcement and repair of soft tissue injuries of the medial meniscus. In repairing and reinforcing medial meniscal defects, the patient must have an intact meniscal rim and anterior and posterior horns for attachment of the mesh. In addition, the surgically prepared site for the CS must extend at least into the red/white zone of the meniscus to provide sufficient vascularization.

The CS reinforces soft tissue and provides a resorbable scaffold that is replaced by the patient's own soft tissue. The CS is not a prosthetic device and is not intended to replace normal body structure.

The ReGen Collagen Scaffold (CS) is a resorbable collagen matrix comprised primarily of bovine type I collagen. The CS is provided in a semi-lunar shape with a triangular cross section to be used to reinforce weakened soft tissue and provide a resorbable scaffold that is replaced by the patient's own tissue. The surgeon trims the device to the size necessary for repair of the damaged or weakened soft tissue.

This document is a 510(k) summary for a medical device (ReGen Collagen Scaffold (CS)), not a study report detailing acceptance criteria and performance. Therefore, most of the requested information regarding study details, sample sizes, and expert qualifications cannot be extracted directly from this document. This document primarily focuses on establishing substantial equivalence to predicate devices.

However, based on the text provided, I can infer and state the following:

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

The document does not explicitly state quantitative acceptance criteria or a direct table of reported device performance against such criteria. The "performance" is implicitly demonstrated through the claim of substantial equivalence to predicate devices based on biomechanical, biocompatibility, animal testing, and clinical studies.

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

• Test Set Sample Size: The document mentions a prospective, randomized multicenter clinical trial that had two separately controlled and randomized arms:
  • Acute arm: 157 patients with no prior surgery to the involved meniscus.
  • Chronic arm: 154 patients with one to three prior treatments to the involved meniscus.
  • Total patients: 311.
• Data Provenance: The study was a "multicenter clinical trial." The document does not explicitly state the country of origin, but given the submission to the FDA in the US, it is likely that the data originated from the US or included US sites. The trial was prospective and randomized.

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 information is not available in the provided text. The document refers to a clinical trial but does not detail how the "ground truth" (e.g., success or failure of the repair) was established by experts, nor does it specify the number or qualifications of such experts.

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

This information is not available in the provided text.

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

This is not applicable. The device is a "Collagen Scaffold" (surgical mesh), not an AI or imaging diagnostic device that would involve human readers or AI assistance.

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

This is not applicable for a physical surgical mesh device.

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

Given that it was a clinical trial for a surgical mesh, the "ground truth" would likely be based on clinical outcomes data related to the reinforcement and repair of soft tissue injuries of the medial meniscus. This would typically involve measures of healing, function, pain, and potentially re-injury rates over the follow-up period.

8. The sample size for the training set

This is not applicable. For a physical medical device like a surgical mesh, there isn't typically a "training set" in the context of machine learning. The clinical trial data served as the primary evidence.

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

This is not applicable for a physical medical device.

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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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