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The ADAPT® Tissue is indicated for use as a patch in the repair of cardiac defects including intracardiac defects, septal defects, valve and annulus repair.

The ADAPT® Tissue medical device is a bovine pericardial patch prepared from glutaraldehyde-crosslinked bovine pericardium using the ADAPT® TEP technology. It is a sterile, light yellow to beige colored, moist, pre-cut sheet of acellular collagen. The products range in size from 4cm² to 84cm² and can range in thickness from 0.25-0.80mm.

This document is a 510(k) Pre-Market Notification from Admedus Regen PTY LTD for their ADAPT® Tissue device. It primarily focuses on establishing substantial equivalence to previously cleared predicate devices (CardioCel and VascuCel) rather than presenting new performance data for the ADAPT® Tissue itself.

Therefore, many of the typical acceptance criteria and study details for an AI/ML device would not apply here. The key criteria for this submission are focused on demonstrating that "ADAPT® Tissue" is essentially the same device as the "CardioCel", with a minor labeling change (refining the indications for use).

However, I can extract the relevant information based on the document's content:

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

Since this is a substantial equivalence submission based on an existing device (CardioCel), the "acceptance criteria" here are that the ADAPT® Tissue should be functionally equivalent to CardioCel. The reported performance refers to the nonclinical testing already performed for CardioCel.

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

This information is not provided in the document as no new human clinical study was performed for the ADAPT® Tissue's specific submission. The submission relies on "nonclinical testing performed in support of the general intended use for CardioCel." Details about the sample size, provenance, and type of study for the original CardioCel testing are not included in this summary.

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 this is not an AI/ML diagnostic or image analysis device relying on expert interpretation for ground truth. It's a biocompatibility and performance assessment of a surgical patch.

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

Not applicable for the reasons stated above.

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 device is a surgical patch, not an AI-assisted diagnostic tool.

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

Not applicable. This device is a surgical patch, not an algorithm.

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

For the "performance" verification of such a device, the "ground truth" would typically involve:

• Biocompatibility testing: In vitro and in vivo studies (animal models) to assess tissue integration, immune response, and toxicity, with outcomes often measured by histological analysis, inflammatory markers, and gross observations.
• Mechanical property testing: Laboratory tests to evaluate tensile strength, elasticity, and suture retention.
• Degradation studies: In vitro and in vivo studies to assess how the material degrades over time in a physiological environment.
• Sterility testing: Laboratory verification of sterility.

These "truths" are established through standard scientific and regulatory testing methods, not expert consensus in the diagnostic sense. The document states "The nonclinical testing performed in support of the general intended use for CardioCel also demonstrates substantial equivalence." This implies a reliance on these types of scientific evaluations performed for the predicate device.

8. The sample size for the training set

Not applicable. This is not an AI/ML device, so there is no training set in that context.

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

Not applicable. For the same reason as above.

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CardioCel 3D is indicated for use as a patch in pericardial closure and the repair of cardiac and vascular defects including intracardiac defects; septal defects, valve and annulus repair; great vessel reconstruction, peripheral vascular reconstruction and suture line buttressing.

The CardioCel 3D device is a bovine pericardial patch prepared from glutaraldehyde-crosslinked bovine pericardium using the ADAPT® TEP technology. It is a sterile, light yellow to beige colored, moist, pre-cut sheet of acellular collagen. It is offered in a 4 x 6 cm size with a 60° curve.

The provided document is a 510(k) premarket notification for a medical device called CardioCel 3D. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving the device meets specific acceptance criteria through a clinical study. Therefore, the document does not contain the detailed information typically found in studies designed to establish device performance against predefined acceptance criteria for AI algorithms.

Here's an analysis based on the information provided, highlighting why certain questions cannot be answered from this document:

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

This document does not specify "acceptance criteria" in the sense of performance metrics for an AI algorithm (e.g., sensitivity, specificity, AUC). Instead, it describes verification and validation testing performed on the physical device.

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

This information is not applicable and therefore not provided. The document describes laboratory verification and validation testing of the physical properties and characteristics of the CardioCel 3D device. It is not presenting data from a "test set" in the context of an AI study.

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 question is not applicable to this document. "Ground truth" and "experts" in this context typically refer to establishing diagnostic certainty for an AI algorithm's performance, which is not what this document addresses. The "ground truth" for the device's physical properties would be established by validated measurement standards and laboratory methods.

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

This is not applicable to this document as no expert adjudication of diagnostic outcomes is involved.

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 to this document. An MRMC study is relevant for evaluating the impact of AI on human interpretation of medical images or data, which is not the purpose of this 510(k) submission for a tissue patch.

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

This is not applicable to this document. There is no algorithm being evaluated in this submission.

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

The "ground truth" for the physical device would be established through scientific measurement standards and validated laboratory methodologies (e.g., precise calipers for dimensional verification, standardized tensile testing equipment and protocols for tensile strength, calibrated burst pressure testers, etc.). This is implicitly assumed for "verification and validation testing."

8. The sample size for the training set:

This is not applicable to this document as no AI algorithm is being trained or evaluated.

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

This is not applicable to this document as no AI algorithm is being trained or evaluated.

Summary of the document's content regarding device performance:

The document indicates that the CardioCel 3D device underwent "Verification and validation testing" which included:

• Dimensional verification
• Crosslink stability
• Tensile testing
• Burst pressure testing

Additionally, a risk assessment evaluated:

• Biocompatibility
• Sterility
• Packaging
• Shelf life

Conclusion: The results of these tests and the risk assessment "demonstrated that the design outputs of the modified device meet the design inputs in conformance with established design controls." This forms the basis for the FDA's determination of substantial equivalence to the predicate device (CardioCel K130872). The key difference from the predicate is that CardioCel 3D holds a curved shape due to a manufacturing change during crosslinking, while maintaining identical intended use, raw materials, tissue processing, sterilization, packaging, and principles of operation.

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VascuCel is indicated as a patch in great vessel repair, peripheral vascular reconstruction and suture line buttressing.

The VascuCel device is a bovine pericardial patch prepared from glutaraldehyde-crosslinked bovine pericardium using the ADAPT® TEP technology. It is a sterile, light yellow to beige colored, moist, pre-cut sheet of acellular collagen. It is offered in a 2 x 8 cm size and a 0.8 x 8 cm size.

This document is a 510(k) summary for VascuCel, a bovine pericardial patch. It does not describe a study proving a device meets acceptance criteria in the typical sense of a clinical trial or algorithm performance study with specific metrics like accuracy, sensitivity, or specificity. Instead, it argues for "substantial equivalence" to a predicate device based on existing clinical literature and verification/validation testing.

Therefore, the following information, based on the provided text, will address the user's request in the context of a 510(k) submission for substantial equivalence. Many requested fields regarding specific performance metrics, sample sizes for test/training sets, expert consensus, and MRMC studies are not applicable or not provided in the context of this type of regulatory submission.

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

Since this is a submission for substantial equivalence based on a predicate device and established clinical practice, the "acceptance criteria" are primarily related to general safety and effectiveness and comparative performance to the predicate. Specific quantitative performance metrics (like sensitivity/specificity for an AI device) are not present.

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

• Test Set Sample Size: Not explicitly stated as a separate "test set" in the context of an algorithm or statistical performance study. The submission refers to "clinical data demonstrating the acute safety of VascuCel" but does not give a sample size, provenance, or whether it was retrospective or prospective. For the predicate device, it mentions "clinical performance data that was performed on CardioCel."
• Data Provenance: Not specified for the "clinical data demonstrating acute safety of VascuCel." The predicate device information (CardioCel) likely drew from various clinical studies and literature, which are not detailed here.

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 not a study requiring expert-established ground truth in the context of image interpretation or diagnostic performance. The device is a patch for surgical repair. The "ground truth" for its safety and effectiveness relies on established medical understanding and clinical outcomes from surgical use.

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

Not applicable. This concept is typically relevant to studies involving multiple expert readers interpreting data for ground truth establishment.

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 device is a surgical patch, not an AI-assisted diagnostic tool.

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

Not applicable. This is a medical device (surgical patch), not an algorithm.

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

The "ground truth" for the claims of safety and effectiveness relies on:

• Established Clinical Literature: The document states, "The safety and effectiveness of bovine pericardial patches for the reconstruction and repair of cardiac and vascular anatomy is well established in clinical literature and is the current standard of care in clinical practice." This implies a broad body of outcomes data and expert consensus over time.
• Verification and Validation Testing: Performed on the predicate device (CardioCel), which would involve various laboratory and potentially animal studies to confirm material properties and biological response.
• Acute Safety Clinical Data: Provided for VascuCel, suggesting actual patient outcome data, though details are absent.

8. The sample size for the training set

Not applicable. This is a medical device, not an AI model requiring a training set.

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

Not applicable.

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