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The Parietene™M Macroporous Mesh is intended for the repair of hemias or other fascial deficiencies that require the addition of a reinforcing material.

The Parietene™ Macroporous Mesh is a non-absorbable synthetic surgical mesh made out of bi-dimensional monofilament polypropylene textile. The Parietene™ Macroporous mesh is offered in a flat sheet and pre-cut mesh. The pre-cut mesh facilitates the repair of inguinal hernias via the anterior approach using a tension free technique.

This submission is for a medical device cleared via a 510(k) pathway, specifically a surgical mesh (Parietene™ Macroporous Mesh). This type of clearance relies on demonstrating substantial equivalence to a predicate device, rather than proving efficacy through clinical trials with specific acceptance criteria as you might see for a diagnostic AI device or a drug.

Therefore, the input document does not contain the kind of information requested in your prompt regarding acceptance criteria, device performance, study designs, sample sizes, expert ground truth, or MRMC studies. These elements are typically found in submissions for AI/ML-based diagnostic devices or complex medical devices requiring de novo or PMA pathways.

Instead, this 510(k) summary focuses on demonstrating that the new device, Parietene™ Macroporous Mesh, is substantially equivalent to legally marketed predicate devices (PROLENE™ Soft (Polypropylene) Mesh (K001122) and Parietene™ Flat Sheet Mesh (K140941)).

Here's a breakdown of what can be extracted and why other parts of your request are not applicable based on this document:

1. Table of acceptance criteria and the reported device performance:

• Acceptance Criteria: The document does not specify quantitative "acceptance criteria" in the way a diagnostic study would (e.g., target sensitivity, specificity). Instead, the acceptance criterion for a 510(k) is demonstrating "substantial equivalence" to a predicate device. This is achieved by showing similar technological characteristics and performance.
• Reported Device Performance: The document lists the types of bench tests performed, but does not provide numerical results for either the subject device or the predicate device that would allow for a direct comparison table. It only states that "The bench results demonstrate that the device is substantially equivalent to the predicate PROLENE™ Soft (polypropylene) Mesh (K001122)."

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

• This information is not provided in the document. Bench testing for medical devices typically involves specific numbers of samples for each test (e.g., n=5 or n=10 per test), but these details are not disclosed in this high-level 510(k) summary. Data provenance (country of origin, retrospective/prospective) is also not applicable as these are laboratory bench tests, not clinical studies.

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

• This is not applicable. This document describes bench testing of a physical medical device, not a diagnostic algorithm that requires expert interpretation for ground truth.

4. Adjudication method for the test set:

• This is not applicable for the same reasons as point 3.

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. This is a physical surgical mesh, not an AI/ML diagnostic device, so MRMC studies are irrelevant.

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

• This is not applicable. This is a physical surgical mesh, not an algorithm.

7. The type of ground truth used:

• For the bench tests, the "ground truth" would be established by standardized laboratory test methods and measurements (e.g., using calibrated equipment to measure thickness, strength, pore size, etc.). It's not based on expert consensus, pathology, or outcomes data in the clinical sense.

8. The sample size for the training set:

• This is not applicable. This is a physical surgical mesh, not an AI/ML algorithm that requires a training set.

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

• This is not applicable for the same reasons as point 8.

In summary, the provided document is a 510(k) summary for a surgical mesh, focusing on demonstrating substantial equivalence through bench testing. It does not contain the detailed clinical study data, expert consensus, or AI/ML-specific information requested in your prompt.

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The Refine™ Support System is indicated for reinforcement of soft tissue in plastic or reconstructive procedures.

The Refine™ Support System consists of a non-absorbable implant and a delivery system. The implant consists of a mesh and suture anchor. The device and the delivery system are delivered sterile and are for single use only.

The Alure Medical Refine™ Support System is a non-absorbable implant and delivery system intended for reinforcement of soft tissue in plastic or reconstructive procedures.

The provided text does not contain the detailed information required to describe the acceptance criteria and a specific study proving the device meets those criteria, as it might for an AI/ML medical device.

This 510(k) summary is for a surgical mesh, a physical medical device, not a software device or an AI/ML algorithm. Therefore, the questions related to AI/ML specific aspects (like test set size, data provenance, ground truth establishment, MRMC studies, standalone performance, training set size, etc.) are not applicable to this document.

However, based on the information provided, here's what can be extracted and inferred about the performance and criteria for this type of device submission:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Not Applicable. This is a physical surgical mesh device, not an AI/ML device requiring a test set of data for performance evaluation in the same manner. The performance evaluation involved "Bench studies and cadaver evaluations." The specific sample sizes for these studies are not provided 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. This is a physical surgical mesh device. The concept of "ground truth" established by experts for a test set, as applied to AI/ML, does not apply here. Device performance likely relies on material testing standards and biomechanical properties rather than expert annotation of data.

4. Adjudication method for the test set

• Not Applicable. See point 3.

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 summary is for a surgical mesh, not an AI-assisted diagnostic or therapeutic device.

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

• Not Applicable. This summary is for a physical surgical mesh, not an algorithm.

7. The type of ground truth used

• Not Applicable / Inferred physical properties. For a surgical mesh, "ground truth" would relate to its physical and mechanical properties (e.g., tensile strength, pore size, biocompatibility) as measured through standardized bench tests and potentially cadaveric studies, rather than expert consensus on data interpretation. The "ground truth" essentially comes from scientific measurement against established standards for surgical meshes.

8. The sample size for the training set

• Not Applicable. This is a physical surgical mesh, not an AI/ML device requiring a training set.

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

• Not Applicable. This is a physical surgical mesh, not an AI/ML device.

Study That Proves the Device Meets Acceptance Criteria

The document references two types of studies used to substantiate the performance and substantial equivalence:

• Bench Studies: These studies would typically involve laboratory testing of the material properties (e.g., tensile strength, burst strength, suture retention strength, porosity, biocompatibility) of the mesh itself and potentially the delivery system. The results of these tests would be compared against the specifications of the predicate devices or established engineering standards.
• Cadaver Evaluations: These studies would involve testing the device in human anatomical cadavers to assess aspects like ease of deployment, fixation strength, tissue interaction, and overall performance in a simulated surgical environment. This would help confirm that the device meets "clinical user needs and intended uses" in a more realistic setting than purely bench models.

The summary states: "Bench studies and cadaver evaluations substantiate that the Refine™ Support System meets user needs and requirements for performance." And further, "Indications for use, technological characteristics, and performance evaluations of the Refine™ Support System show that the device is substantially equivalent to the predicate devices. Any differences are minor and do not raise any additional concerns regarding safety or effectiveness of the device."

Conclusion:

For this type of physical device, the "acceptance criteria" are intrinsically tied to demonstrating substantial equivalence to existing, legally marketed predicate devices in terms of indications for use, technological characteristics, and performance. The "study" consisted of bench studies and cadaver evaluations which served to confirm that the Refine™ Support System's materials, specifications, and function are comparable to its predicates and meet the stated intended use without raising new safety or effectiveness concerns. The specific numerical data, methodologies, and detailed results of these studies are not disclosed in this 510(k) summary, which is typical for this type of FDA submission.

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