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The Surgisis® Sling is intended for implantation to reinforce soft tissues where weakness exists in the urological, gynecological and gastroenterological anatomy including but not limited to the following procedures: transvaginal repair of stress urinary incontinence, such as pubourethral support and bladder support, and transabdominal repair of apical vaginal prolapse, colon and rectal prolapse, and sacrocolposuspension. By providing pubourethral support, the sling may be used for the treatment of urinary incontinence resulting from urethral hypermobility or intrinsic sphincter deficiency. The device is supplied sterile and is intended for one-time use.

The Surgisis® Sling is supplied in sheet form in sizes ranging from 20 cm2 to 140 cm2. The device is packaged in sterile, sealed double pouches.

This document is a 510(k) Summary for the Surgisis® Sling, a surgical mesh device. It does not provide information about acceptance criteria or a study proving that the device meets such criteria in terms of performance metrics like sensitivity, specificity, or any other quantitative measures typically associated with AI/ML device evaluations.

The document focuses on demonstrating substantial equivalence to predicate devices based on:

• Intended Use: The Surgisis® Sling has the same intended use as its predicates (K980431, K954665, K980483, K963226).
• Technological Characteristics: The device is stated to have the same technological characteristics as the predicate devices.
• Testing for Biocompatibility, Integrity, and Performance: The document states that the Surgisis® Sling "passed the requirements of all tests" in these areas. However, it does not specify what these requirements or performance metrics were, nor does it provide the results of these tests in a detailed manner.

Therefore, I cannot provide the requested information for an AI/ML device as this document pertains to a traditional medical device (surgical mesh) where the evaluation criteria are different. The concept of "acceptance criteria" in this context refers to meeting the standards of biocompatibility, integrity, and equivalent performance to existing devices, rather than statistical performance metrics for an AI algorithm.

Summary of what can be extracted, and what cannot:

1. Table of acceptance criteria and reported device performance:

   • Acceptance Criteria (Implied): Biocompatibility, Integrity, Performance (in relation to predicate devices).
   • Reported Device Performance: "The Surgisis® Sling passed the requirements of all tests." No specific quantitative performance metrics are provided.

2. Sample size used for the test set and data provenance:

   • Not applicable/Not provided. The document refers to "tests" on the device itself (e.g., material properties), not a "test set" of patient data for an algorithm.

3. Number of experts used to establish the ground truth for the test set and qualifications of those experts:

   • Not applicable/Not provided. Ground truth in the context of an AI/ML device would involve annotations by experts on medical data. This document doesn't involve that.

4. Adjudication method:

   • Not applicable/Not provided.

5. Multi-reader multi-case (MRMC) comparative effectiveness study:

   • Not applicable/Not performed. This is for evaluating AI assistance to human readers.

6. Standalone (algorithm only) performance:

   • Not applicable/Not performed. This device is not an algorithm.

7. Type of ground truth used:

   • Not applicable in the AI/ML sense. The "ground truth" for this device would be established engineering and biological standards for surgical mesh materials.

8. Sample size for the training set:

   • Not applicable/Not provided. This document describes a physical medical device, not an AI algorithm.

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

   • Not applicable.

In conclusion, the provided document describes a premarket notification (510(k)) for a conventional surgical mesh, focusing on substantial equivalence to existing devices. It does not contain the type of information related to acceptance criteria, test sets, ground truth, or study designs that would be relevant for an AI/ML medical device.

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The Glycar Pericardial Patch and Glycar Vascular Patch of the Glycar Tissue Repair Patch line of products are intended for pericardial closure, peripheral vascular reconstruction and repair, and cardiac and great vessel reconstruction and repair.

The Glycar Vascular Repair Patches are made of the same material as the predicate device, the Peri-Guard™ tissue patches, which are made from glutaraldehyde-treated bovine pericardium.

Here's an analysis of the provided text regarding the Glycar Vascular Repair Patch's acceptance criteria and substantiating study information:

The provided text is a 510(k) summary for a medical device called the Glycar Vascular Repair Patch. It outlines the device's characteristics and its proposed equivalence to a predicate device. However, it does not contain information about a study designed to prove the device meets specific acceptance criteria in the way a clinical trial or performance study would.

Instead, the submission focuses on demonstrating substantial equivalence to an already approved predicate device, the Bio-Vascular Inc., Vascu-Guard™ peripheral vascular patch (K942010), and other Glycar products (K942911, K954665). The "acceptance criteria" here are effectively the characteristics and performance of the predicate device, which the Glycar product aims to match or be comparable to.

Therefore, many of the requested sections (sample size, experts, adjudication, MRMC, standalone, training set, ground truth for training) are not applicable as the submission is a demonstration of substantial equivalence, not a de novo performance study with acceptance criteria.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Not Applicable. The submission is a demonstration of substantial equivalence, not a performance study with a "test set" in the context of typical algorithm validation. Performance characteristics (like strength, pyrogenicity, SAL) are asserted to be equivalent to the predicate, likely based on internal testing or existing validation data, but not explicitly as a test set for this 510(k). The mention of internal data like "Supporting Data" and "Validation of Sterilization Processes" suggests data exists but is not elaborated upon in this summary.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not Applicable. See point 2.

4. Adjudication method

• Not Applicable. See point 2.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done

• Not Applicable. This is a medical device for surgical repair (a patch), not an imaging-based AI 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

• Not Applicable. This is a physical medical device, not an algorithm.

7. The type of ground truth used

• Based on Comparison to Predicate Device Characteristics and Internal Testing Standards. The "ground truth" for this submission are the established performance and safety characteristics of the predicate device (Vascu-Guard™) and the conformity to recognized standards for materials (e.g., glutaraldehyde-treated bovine pericardium), sterilization (10^-6 SAL), and biological safety (pyrogenicity, BSE-free source). These are verified through various internal tests and comparisons referenced in the submission (e.g., "See Comparison Chart, Chart 1, in Addenda under Substantial Equivalence and see Supporting Data").

8. The sample size for the training set

• Not Applicable. This is a physical medical device, not an AI/ML algorithm that requires a training set.

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

• Not Applicable. See point 8.

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The Glycar Pericardial Patch and Glycar Vascular Patch of the Glycar Tissue Repair Patch line of products are intended for pericardial closure, peripheral vascular reconstruction and repair, and cardiac and great vessel reconstruction and repair.

The Glycar Pericardial Patches are made of the same material as the predicate device, the Peri-Guard™ tissue patches, which are made from glutaraldehyde-treated bovine pericardium.

The provided text describes a 510(k) submission for the Glycar Pericardial Patch. A 510(k) submission is for demonstrating substantial equivalence to a predicate device, not typically for proving a device meets specific acceptance criteria through a study with performance metrics in the same way a PMA (Premarket Approval) would. Therefore, the document does not contain a study that explicitly demonstrates the device meets quantitative acceptance criteria with reported device performance as would be expected for a novel device.

Instead, the submission focuses on demonstrating substantial equivalence to a predicate device, the Bio-Vascular Inc. Peri-Guard™ patch. This demonstration relies on comparing material composition, intended use, sterilization methods, and other performance features.

Here's a breakdown of the information requested, based on the provided text, and highlighting where the information is not present due to the nature of a 510(k) submission.

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

As this is a 510(k) submission for substantial equivalence, the "acceptance criteria" are primarily related to matching or exceeding the predicate device's characteristics and performance attributes rather than specific, quantitative clinical outcomes from a new study. The document lists several performance features that need to be "equal to or better than" the predicate device. However, it does not provide a table with specific numerical acceptance criteria and a corresponding quantitative "reported device performance" from a dedicated study.

The document mentions "See in Addenda , Chart 1, Comparison Chart, under Substantial Equivalence and Breaking Strength under Supporting Data" and "Pyrogenicity under Supporting Data." This suggests that specific data on breaking strength and pyrogenicity would be provided in the full addenda to demonstrate these "other performance features." However, these specific numerical results are not included in the excerpt provided.

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 provided in the excerpt. A 510(k) submission typically relies on existing predicate device data, preclinical bench testing, and sometimes animal studies for substantial equivalence. It does not ordinarily involve a prospective clinical study with a "test set" in the sense of a pivotal trial for a novel device. The "Supporting Data" mentioned for breaking strength and pyrogenicity would likely be from bench tests or animal studies, but the sample sizes, methodology, and provenance are not detailed here.

The source of the bovine pericardium (raw material) is from "government approved abbatoirs in Transvaal, S. Africa," and is certified BSE-free. This refers to the source of the biological material, not clinical data provenance.

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 applicable and therefore not provided in the excerpt. As this is a medical device (tissue patch) for surgical repair, the concept of "ground truth for a test set" established by experts, as seen in AI/diagnostic imaging studies, does not apply. Performance is assessed through material properties, biocompatibility, and manufacturing controls, compared to a predicate. Clinical outcomes, if gathered, would be observed through surgical use over time, not via expert interpretation of a "test set."

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

This information is not applicable and therefore not provided. The device's performance is not being evaluated through expert adjudication of cases, as would be common in diagnostic imaging or clinical trials.

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

There is no mention of an MRMC study. This type of study (comparative effectiveness of human readers with/without AI assistance) is specific to AI-powered diagnostic tools or imaging analysis and is not relevant to a biological tissue patch.

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

This is not applicable and therefore not provided. The Glycar Pericardial Patch is a physical medical device (a tissue patch), not an algorithm or software. Therefore, there is no "standalone performance" of an algorithm without human involvement.

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

Given the nature of the device (a tissue patch), the "ground truth" for demonstrating substantial equivalence primarily relies on:

• Material Characterization: Chemical composition (glutaraldehyde-treated bovine pericardium).
• Physical Property Testing: Bench testing for tissue strength (e.g., breaking strength).
• Biocompatibility Testing: Pyrogenicity tests.
• Sterility Assurance: Sterility Assurance Level (SAL) verification.
• Predicate Device History: The predicate device's long-standing safe use ("over ten years without reported material failure") serves as critical evidence supporting the safety and effectiveness of the material for the stated intended use.
• Manufacturing Process Controls: Adherence to established manufacturing and sterilization protocols.

There is no mention of "expert consensus," "pathology," or specific "outcomes data" from a new clinical study. The FDA's 510(k) approval also explicitly states: "Please be advised that you may not make any claims about the effectiveness of the anticalcification treatment or durability of the device in any labeling or advertisements for this device until results of human clinical studies are submitted and found to substantiate such claims." This indicates that post-market (or future PMAs) studies would be needed for such specific effectiveness claims, but not for the initial 510(k) clearance based on substantial equivalence.

8. The sample size for the training set

This is not applicable as the Glycar Pericardial Patch is a physical medical device, not an AI model requiring a training set.

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

This is not applicable as there is no training set for this type of device.

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The Glycar Inc., Tissue Repair Patch family of products have the same intended uses as the predicate device, the Bio-Vascular Inc., Peri-Guard™ family of products, which are all tissue patches made from the same glutaraldehyde-treated bovine pericardium.

Already approved intended uses for the Glycar Tissue Repair Patch family of glutaraldehyde-tanned bovine pericardial tissue patches:

The Glycar Tissue Repair Patch (K942911) was deemed substantially equivalent to the Peri-Guard™ bovine pericardial tissue patch and received approval for U.S. marketing for the intended use of hernia and other intraabdominal soft tissue defect or deficiency repair in December, 1994.

An addition to the Glycar Tissue Repair Patch family of products, the Glycar Staple Strip (K954665) was deemed substantially equivalent to the Peri-Strips™ bovine pericardial surgical staple bolsters and received approval for U.S. marketing for the intended uses of surgical stapling of lung tissue, gastric staplings, rectal and vaginal prolapse, urethral sling, reconstruction of the pelvic floor, and hernias or defects of the diaphragm, thoracic and abdominal wall in October, 1995.

The Glycar Staple Strip uses the identical material as the Glycar Tissue Repair Patch with the addition of a synthetic suture material to assist placement of the device on the surgical stapling arm.

The Glycar Tissue Repair Patches are made of the same material as the predicate device, the Peri-Guard™ tissue patches, which are made from glutaraldehyde-treated bovine pericardium.

Glutaraldehyde-treated bovine pericardium is a strong, pliable, biocompatible material which is easy to handle surgically.

Glycar's glutaraldehyde-treated pericardium possesses tissue strength equaling that of the predicate device. It provides for a durable repair, replacement or reinforcement of natural soft tissue.

The manufacturing process for the Glycar Tissue Repair Patches involves the harvesting of bovine pericardium, fixation of the pericardium in glutaraldehyde, inspection and cleaning of the pericardium, selection of the pericardium for use in strip production, additional tanning of the bovine pericardium, and then cutting of the glutaraldehyde-treated bovine pericardium for the designated strip size, followed by sterilization in formaldehyde, followed then by treatment with Glycar's proprietary anti-inflammation treatment, XX.

Sterility is maintained by using aseptic fill under Clean Room 100 conditions, during transfer to antiinflammation treatment and to the final storage medium, 2% propylene oxide in water.

Sizes and shapes will be specific for product line reflecting different indications as appropriate for the use.

The provided text is a 510(k) summary for a medical device (Glycar Tissue Repair Patch) and does not describe a study involving acceptance criteria and device performance in the context of diagnostic accuracy, which is typically what the requested table and study details apply to.

Instead, this document focuses on demonstrating substantial equivalence to a predicate device (Peri-Guard™ product line, including Supple Peri-Guard™). The "acceptance criteria" here are essentially the demonstration that the Glycar Tissue Repair Patches are sufficiently similar to the predicate device in terms of material, intended use, sterilization, and other performance features. The "study" proving this is a comparison to the predicate device, not a diagnostic performance study.

Therefore, many of the requested fields are not applicable because the input describes a regulatory submission for a tissue repair patch, not a diagnostic AI device requiring a clinical performance study with human readers and ground truth.

However, I can extract the relevant information regarding the comparison made to establish substantial equivalence.

1. Table of Acceptance Criteria and Reported Device Performance

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

Not applicable in the context of a diagnostic device performance study. The "test set" here refers to the Glycar device being compared against the predicate. The "data provenance" refers to the manufacturing process, material sourcing, and test results for the Glycar device itself to demonstrate its properties.

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

Not applicable. Ground truth, in this context, would essentially be the validated performance and safety profile of the predicate device, against which the new device is compared. This is not established by a panel of experts for a specific diagnostic task.

4. Adjudication Method for the Test Set

Not applicable. This is not a study requiring adjudication of diagnostic findings.

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 is a comparison of a tissue repair patch to a predicate device, not a diagnostic AI system with human readers.

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

Not applicable. This is a medical device, not an algorithm.

7. The Type of Ground Truth Used

The "ground truth" for this substantial equivalence claim is the established safety and effectiveness profile of the predicate device (Peri-Guard™ product line), as approved by the FDA (K923657). The Glycar device demonstrates that it meets essentially identical specifications and performance characteristics, thus proving it is "as safe and effective" as the predicate.

8. The Sample Size for the Training Set

Not applicable. This is not a machine learning model requiring a training set.

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

Not applicable.

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