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Restorelle polypropylene mesh may be used for the repair of abdominal wall hernia, including inguinal, femoral, and incisional, and uterovaginal prolapse and other fascial deficiencies that require support material. It may be used in open or laparoscopic abdominal procedures or for repair by the vaginal route.

Restorelle is a non-absorbable polypropylene mesh constructed from knitted monofilaments of extruded polypropylene. Restorelle polypropylene mesh is constructed using a warp-knit process that permits the mesh to be cut into desired shape or size without unraveling. It maintains excellent isotropic properties arising from its knitted construction. Restorelle polypropylene mesh has the necessary strength, flexibility, durability and surgical adaptability properties which permit the correct adaptation to the various stresses encountered in the body. The device is supplied sterile.

The provided text is a 510(k) summary for the Restorelle™ polypropylene mesh and does not contain information about a study that proves the device meets specific acceptance criteria in the manner of a performance study with a test set, ground truth, or human reader evaluations.

Instead, this document focuses on demonstrating substantial equivalence to predicate devices through non-clinical testing.

Here's a breakdown based on the information available:

Acceptance Criteria and Device Performance

The document does not explicitly state "acceptance criteria" in terms of specific performance metrics (like accuracy, sensitivity, specificity, etc.) with numerical targets for a device under evaluation. Instead, the "acceptance criteria" appear to be broadly demonstrating that the device is "substantially equivalent" to predicate devices through various technical and material properties.

Study Details for this 510(k) Submission:

This 510(k) summary describes a submission for substantial equivalence based on non-clinical (bench) testing and comparison to predicate devices, not a clinical performance study involving patient data, ground truth labels, or human reader evaluations in the context of AI/diagnostic device assessment.

Here's why many of the requested items are not applicable or cannot be answered from this document:

1. Sample size used for the test set and the data provenance: Not applicable. The "test set" in this context refers to the physical mesh samples subjected to in-vitro (bench) mechanical and biocompatibility tests, not a dataset of patient images or clinical cases. The document does not specify the number of samples used for these bench tests, nor does it discuss data provenance in terms of country of origin or retrospective/prospective for clinical data.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. There is no "ground truth" established by experts in the context of this bench testing for a polypropylene mesh. The "truth" is determined by the physical properties measured in the lab.

3. 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 a diagnostic or AI performance study with expert readers.

4. 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 a study involving human readers or AI assistance.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithm or software device.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable in the context of clinical "ground truth." The "ground truth" for the non-clinical tests would be the established physical properties and biocompatibility standards required for medical devices of this type, as demonstrated by the predicate devices.

7. The sample size for the training set: Not applicable. There is no AI algorithm being trained.

8. How the ground truth for the training set was established: Not applicable.

In summary: This document clearly states, under "Summary Of Clinical Tests Submitted (As Applicable)," that it is "Not applicable." The entire submission is based on non-clinical (in-vitro bench) tests and comparison of technological characteristics to already-approved predicate devices to demonstrate substantial equivalence, rather than a clinical performance study involving patient data or AI algorithm validation.

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RESTORELLE™ polypropylene mesh may be used for the repair of abdominal wall hernia, including inguinal, femoral, and incisional, and uterovaginal prolapse and other fascial deficiencies that require support material. It may be used in open or laparoscopic abdominal procedures or for repair by the vaginal route.

Restorelle™ is a non-absorbable polypropylene mesh constructed from knitted monofilaments of extruded polypropylene.

Restorelle™ polypropylene mesh is constructed using a warp-knit process to a unique design that permits the mesh to be cut into any desired shape or size without unraveling.

It maintains excellent isotropic properties arising from its knitted construction.

Restorelle™ polypropylene mesh has the necessary strength, flexibility, durability and surgical adaptability properties which permit the correct adaptation to the various stresses encountered in the body.

The device is supplied sterile.

The Mpathy Medical Devices, Ltd. Restorelle polypropylene mesh is a surgical mesh device. The provided text, a 510(k) summary, does not describe a study involving an algorithm or AI to meet acceptance criteria. Therefore, most of the requested information regarding AI-specific criteria and studies cannot be extracted from this document.

The document focuses on demonstrating substantial equivalence to predicate devices through biocompatibility and mechanical testing for the physical mesh product itself, not a digital diagnostic or AI device.

Here's a breakdown based on the provided text, highlighting what is (and isn't) present:

1. A table of acceptance criteria and the reported device performance
(See table above for the general areas of performance comparison). The document states the device has "the necessary strength, flexibility, durability and surgical adaptability" but does not provide specific quantitative metrics or acceptance criteria for these characteristics. The primary performance claim is "substantial equivalence" to predicate devices.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
Not applicable. This is for a physical medical device (surgical mesh), not a diagnostic algorithm or AI. The testing performed was biocompatibility and mechanical testing of the mesh material itself. The document does not specify sample sizes for these tests, nor the country of origin of testing data.

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, as this is not a diagnostic device or AI.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable, as this is not a diagnostic device or AI.

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, a MRMC study was not done. This device is a surgical mesh, not an AI or imaging diagnostic tool that would involve human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
No, a standalone algorithm performance study was not done. This is a physical medical device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
Not applicable in the context of an AI device. For the physical mesh, the "ground truth" for its properties would be established through standard material and biocompatibility testing per ISO standards or similar, comparing it to the known properties of the predicate devices.

8. The sample size for the training set
Not applicable, as this is not an AI or algorithm.

9. How the ground truth for the training set was established
Not applicable, as this is not an AI or algorithm.

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