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The MatriStem UBM™ Pericardial Patch is intended for the reconstruction and repair of the pericardium.

The MatriStem UBM Pericardial Patch is a 4-layer multi-laminate device comprised of stacked urinary bladder matrix (UBM) sheets. The Pericardial Patch serves as a patch to reconstruct the pericardium and restore the native anatomy. The Pericardial Patch is available in three sizes: 7x10 cm, 7x15 cm, and 10x15 cm. Each device is packaged in a double peel-open pouch and an outer carton. The device is terminally sterilized using electron-beam irradiation.

This document describes the regulatory submission for the MatriStem UBM™ Pericardial Patch, a medical device intended for the reconstruction and repair of the pericardium. It is a 510(k) premarket notification, which means the manufacturer (ACell, Inc.) is seeking to demonstrate that their device is substantially equivalent to legally marketed predicate devices, and therefore does not require a full premarket approval (PMA).

The information provided covers bench testing and an animal study to support the substantial equivalence claim. However, it's important to note that this submission does not involve an AI/ML-based device and therefore the questions relating to AI-specific acceptance criteria, multi-reader multi-case studies, expert adjudication methods, and training/test set ground truth establishment for an AI algorithm are not applicable to this document. The device is a biological patch, not a diagnostic or prognostic AI tool.

Therefore, many of the requested items regarding AI/ML aspects will be answered as "Not Applicable" or "No" as the study described is for a physical medical device.

Here's the breakdown based on the provided text:

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

Since this is a physical medical device and not an AI model, the acceptance criteria are based on physical, chemical, and biological properties, and functionality in an in vivo model.

Note: Specific quantitative acceptance criteria for most bench tests are not provided in this public summary but are typically part of internal design control documentation and would have been submitted to the FDA. The document states "All acceptance criteria were met" for the animal study, indicating the successful completion of the pre-defined endpoints.

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

• Test Set Sample Size (Animal Study):

  • Total pigs: 12
  • Sham group (no repair): 2 pigs
  • ACell MatriStem UBM Pericardial Patch group: 5 pigs
  • Predicate CorMatrix Pericardial Patch group: 5 pigs

• Data Provenance: The study was a "90-day Good Laboratory Practices (GLP) animal study" performed in a porcine (pig) model. The country of origin is not specified but generally, GLP studies adhere to international standards. It is a prospective animal study designed to evaluate the device.

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

• Not Applicable (N/A) for an AI/ML context. This is a physical medical device.
• For the animal study, the ground truth was established through objective biological and physiological assessments:
  • Histology evaluation: Performed by pathologists.
  • Full necropsy examination: Performed by veterinary pathologists.
  • Echocardiography measurements: Performed by specialists in echocardiography (likely veterinary cardiologists or trained technicians).
  • The document implies that these assessments were performed by qualified personnel as part of a GLP study, but the specific number and qualifications of individuals are not detailed in this summary.

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

• N/A for an AI/ML context. This pertains to consensus among human readers for image interpretation, which is not relevant for this device.
• For the animal study, the "adjudication" of results would rely on standardized GLP protocols for sample collection, pathology, and data analysis to ensure consistency and objectivity. Any disagreements in interpretation (e.g., in histology slides) would typically be resolved by senior pathologists or consensus panels within the pathology group, but this is not an "adjudication method" in the AI sense.

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, an MRMC study was not done. This is a study for a physical implantable medical device, not an AI diagnostic/assistance tool.
• The comparative effectiveness was demonstrated by comparing the subject device to a predicate device and a sham in an animal model based on biological and physiological outcomes, not human reader performance.

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

• N/A. This is a physical medical device, not an algorithm.

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

• Pathology and Outcomes Data (from an animal model).
  • Histology evaluation: Provides microscopic ground truth on tissue response, cellular infiltration, and remodeling.
  • Full necropsy examination: Provides macroscopic ground truth on device integration and tissue health.
  • Echocardiography measurements: Provides functional ground truth on cardiac performance.
  • The "ground truth" for the device's performance in vivo was established by these objective biological and physiological assessments in directly implanted animals.

8. The sample size for the training set

• N/A. This is a physical medical device. There is no "training set" in the context of machine learning.
• The development process for this device would involve extensive internal research and development, materials characterization, and process optimization, but this does not constitute a "training set" for an AI model.

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

• N/A. As there is no AI training set, this question is not applicable.

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Gentrix Surgical Matrix Thick and Gentrix Surgical Matrix Extend are intended for implantation to reinforce soft tissue where weakness exists in patients requiring gastroenterological or plastic & reconstructive surgery. Reinforcement of soft tissue within gastroenterological and plastic & reconstructive surgery includes, but is not limited to, the following procedures: hernia and body wall repair, colon and rectal prolapse repair, and esophageal repair.

The Gentrix™ Surgical Matrix Thick and Gentrix™ Surgical Matrix Extend devices are composed of porcine-derived extracellular matrix scaffolds, specifically known as urinary bladder matrix. The devices are supplied in an eight-layer sheet configuration in sizes up to 30 cm x 40 cm, and packaged in double peel-open pouches. The devices are terminally sterilized using electron beam irradiation. The implantable biomaterial is a resorbable extracellular matrix scaffold that will incorporate (remodel) into the body through cellular infiltration, capillary growth, and integration by the surrounding host tissue. Animal studies have shown device resorption in approximately 240 days.

This document is a 510(k) premarket notification for a medical device called Gentrix™ Surgical Matrix Thick and Gentrix™ Surgical Matrix Extend. This device is a surgical mesh made from porcine urinary bladder matrix, intended for reinforcing soft tissue in various surgical procedures.

The information provided focuses on demonstrating "substantial equivalence" to a previously cleared predicate device (Gentrix™ Surgical Matrix 8-Layer, K162554), rather than establishing novel acceptance criteria against a clinical outcome or a new performance standard. Therefore, the concept of "acceptance criteria" in the context of an AI/ML device's performance metrics (like sensitivity, specificity, etc.) is not directly applicable here. The "reported device performance" is primarily comparative to the predicate device.

However, I can extract the types of tests performed and the general findings which serve as proof of substantial equivalence.

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

Since this is a substantial equivalence submission for a traditional medical device (surgical mesh) and not an AI/ML device, the "acceptance criteria" are not reported as specific performance metrics (like sensitivity, specificity, or AUC with threshold values). Instead, the acceptance criteria are implicitly that the new devices perform equivalently to the predicate device across various tests.

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

• Bench Testing (Biocompatibility, Mechanical, Material Characterization): Not specified in terms of sample size or data provenance. These are typically in-house laboratory tests on material samples.
• Animal Testing: A "pre-clinical porcine model" was used. The number of animals or specific details of the study are not provided. Data provenance is implied to be from a laboratory setting.
• Clinical Data: "No Clinical data was provided in support of this clearance." This implies a sample size of zero for human clinical trials.

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 submission does not involve ground truth established by experts for performance evaluation in the way an AI/ML device would. The evaluation is based on laboratory and animal tests against established scientific and engineering principles for surgical mesh materials.

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

Not applicable. There is no adjudication method described as this is not an AI/ML device or a study involving human readers/interpreters.

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 not an AI/ML device.

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

Not applicable. This is not an AI/ML device.

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

The "ground truth" for this device's performance is based on:

• Established scientific standards for biocompatibility (e.g., ISO-10993).
• Direct measurement of physical properties (tensile strength, tear strength, stiffness, etc.) of the device material.
• Biological observations in an animal model (tissue integration, remodeling, degradation).
• Comparison to the known and accepted performance characteristics of 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.

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

Not applicable. This is not an AI/ML device.

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