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The SURGIMESH®XB - Skirted mesh is intended for use in the reinforcement of tissues during surgical repair.

The SURGIMESH®XB-Skirted is indicated for the reconstruction of hernias and soft tissue deficiencies and for the temporary bridging of fascial defects. The silicone layer minimizes the tissue attachment to the mesh in the case of direct contact with the viscera

The SURGIMESH®XB - Skirted surgical mesh is a non-absorbable, synthetic mesh, made of non-knitted, non-woven fibers of polypropylene, one surface of which is coated with silicone. The use of SURGIMESH®XB - Skirted mesh provides reinforcement of soft tissues. On the opposite side, the silicone layer minimizes tissue attachment to the mesh in case of direct contact with the viscera. The SURGIMESH®XB - Skirted mesh is supplied sterile and is available in anatomic forms in order to meet individual surgeons' needs.

More specifically, the SURGIMESH® XB - Skirted mesh is composed of a layer of non-woven, non-knitted material made from polypropylene (i.e., SURGIMESH®XB), a skirted part made from polypropylene (i.e., SURGIMESH®WN) linked with the first layer by sewing a PVDF thread into the two parts with a layer of silicone (equivalent to SURGIMESH® XB product material).

This document describes the premarket notification 510(k) for the SURGIMESH®XB - Skirted surgical mesh. The provided text outlines the device's characteristics, intended use, and the performance testing conducted to demonstrate substantial equivalence to predicate devices, rather than a study against a specific set of acceptance criteria with human readers or standalone algorithm performance.

Here's an analysis of the available information regarding acceptance criteria and supporting studies, formatted as requested, and noting where information is not present in the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not specified. The document states "Preclinical testing was conducted" but does not detail the number of units or samples tested for each criterion.
• Data Provenance: The studies were preclinical testing conducted by Aspide Medical. The location of these tests (e.g., country of origin) is not explicitly stated, but Aspide Medical is based in LA TALAUDIERE (FRANCE). The studies are retrospective as they summarize testing that has already been performed.

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

This information is not applicable to the provided document. The performance data focuses on technical and material characteristics of a surgical mesh, not on expert interpretations of medical images or clinical outcomes that typically require ground truth established by experts.

4. Adjudication Method for the Test Set

This information is not applicable. Adjudication methods are typically used in studies involving human interpretation or clinical outcomes where there might be disagreement in assessments. The reported tests are objective technical validations against established standards.

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 information is not applicable. This document describes a medical device (surgical mesh), not an AI-powered diagnostic or assistive technology that would involve human readers or AI assistance.

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

This information is not applicable. This document describes a medical device (surgical mesh), not an AI algorithm.

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

The "ground truth" for the performance testing cited are the established international standards (ISO, USP) for biocompatibility, sterility, packaging, and material characterization. The device's performance was compared against these universally recognized technical standards and against the specifications of predicate devices.

8. The Sample Size for the Training Set

This information is not applicable. This document describes a medical device (surgical mesh) testing and substantial equivalence, not the development or training of an AI model that would require a training set.

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

This information is not applicable. As no AI model or training set is mentioned, there is no ground truth for a training set to be established.

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The SURGIMESH®XD mesh is recommended for reinforcement of hernia defects. The hernia repair is for an inguinal hernia. The SURGIMESH®XD implant is indicated for use via an extraperitoneal approach either by open or laparoscopic surgery.

The SURGIMESH®XD consists of non-absorbable synthetic mesh, made of polypropylene. The SURGIMESH®XD has a 3D shape to fit the inguinal anatomy. In addition, two translucent windows are incorporated into the SURGIMESH®XD implant to allow proper positioning of the implant. The two windows allow the surgeon to place the implant in the correct anatomical location using the Cooper's ligament and the spermatic cord as anatomical guides in repairing a hernia defect. SURGIMESH®XD mesh is supplied sterile.

The provided text describes a 510(k) premarket notification for a surgical mesh device, SURGIMESH®XD. It focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed acceptance criteria and a comprehensive study proving the device meets those criteria.

Therefore, much of the requested information regarding specific acceptance criteria, device performance, sample sizes, expert ground truth, adjudication methods, MRMC studies, standalone performance, training set details, and ground truth establishment for a study proving the device meets acceptance criteria cannot be extracted directly from this document. The document describes pre-clinical testing for safety and materials, which are typical for substantial equivalence claims.

Here's a breakdown of what can and cannot be extracted:

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

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 document. The text refers to "preclinical testing" but does not specify sample sizes for these tests or the 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 provided. The document describes physical and material testing, not diagnostic or interpretive tasks that would require human expert ground truth.

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

This information is not provided, as the testing described doesn't involve human interpretation requiring adjudication.

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:

An MRMC study was not conducted or described. This document pertains to a medical device (surgical mesh) that does not incorporate AI or require human interpretive reading in the way an AI-powered diagnostic tool would.

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

A "standalone" performance in the context of an algorithm is not applicable here, as the device is a physical surgical mesh and not an algorithm.

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

The "ground truth" for the performance tests mentioned would be the defined standards themselves (e.g., ISO standards for biocompatibility or sterility, or the physical characteristics of the predicate devices for material characterization). For functionality, it would be the expected function of the device under test conditions.

8. The sample size for the training set:

This information is not applicable/not provided. The device is not an AI algorithm that requires a training set.

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

This information is not applicable/not provided. The device is not an AI algorithm that requires a training set.

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The SURGIMESH®XB is intended to be used for the reinforcement of tissues during surgical repair.

The SURGIMESH®XB is indicated for use in: the reconstruction of hernias and soft tissue deficiencies and for the temporary bridging of fascial defects. The silicone layer minimizes the tissue attachment to the mesh in the case of direct contact with the viscera.

The SURGIMESH®XB surgical mesh is a non-absorbable synthetic mesh, made of nonknitted, non-woven fibers of polypropylene, one surface of which is coated with silicone. The SURGIMESH®XB mesh is supplied sterile and is available in anatomic forms in order to meet the individual patient's surgical needs. The use of SURGIMESH®XB mesh provides reinforcement of soft tissues. On the opposite side, the silicone layer minimizes tissue attachment to the mesh in case of direct contact with the viscera.

This 510(k) summary is for a medical device (SURGIMESH®XB surgical mesh) and primarily focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a study with specific acceptance criteria and performance metrics in the way a diagnostic AI device submission would.

Therefore, many of the requested categories are not applicable or cannot be extracted from this type of regulatory document.

Here's an attempt to address the points based on the provided text, indicating "Not Applicable" or "Not Provided" where the information is not present:

Acceptance Criteria and Study for SURGIMESH®XB

The submission for SURGIMESH®XB is a 510(k) premarket notification, which establishes substantial equivalence to legally marketed predicate devices. This type of submission relies on demonstrating that the new device has the same intended use, similar technological characteristics, and that any differences do not raise new questions of safety or effectiveness. It does not typically involve defining specific performance acceptance criteria (e.g., accuracy, sensitivity, specificity) in the same way an AI diagnostic algorithm would, nor does it present a clinical trial with a test set, ground truth experts, or MRMC studies.

Therefore, the "acceptance criteria" for a device like SURGIMESH®XB are implicitly tied to demonstrating that its mechanical, material, and biocompatibility characteristics are "substantially equivalent" to predicate devices, which are themselves deemed safe and effective.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not Applicable. This submission does not describe a clinical "test set" in the context of diagnostic performance. The evaluation is based on material testing and comparison to predicates.
• Data Provenance: Not Applicable. No patient data or clinical study dataset is described.

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

• Number of Experts: Not Applicable. No ground truth establishment by experts is described for this type of device submission.
• Qualifications of Experts: Not Applicable.

4. Adjudication Method for the Test Set

• Adjudication Method: Not Applicable. No test set requiring expert adjudication is described.

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

• MRMC Study: No. This is a surgical mesh, not an AI diagnostic device for human readers.
• Effect Size: Not Applicable.

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

• Standalone Performance: Not Applicable. This is a surgical mesh, not an algorithm.

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

• Type of Ground Truth: Not Applicable. The "ground truth" concept for a surgical mesh revolves around proven safety and effectiveness of similar devices and the demonstration of equivalent physical, mechanical, and biological properties. It's not based on case-level diagnoses or outcomes in a study, but rather on material science and biocompatibility testing.

8. The Sample Size for the Training Set

• Sample Size: Not Applicable. This is a surgical mesh, not an AI device that uses a training set.

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

• Ground Truth Establishment: Not Applicable.

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The SURGIMESH®WN implant is recommended for reinforcement of hernia defects. The hernia repair could be, for example, inguinal hernia, femoral or crural hernia or ventral hernia. The SURGIMESH®WN implant is indicated for use via an extraperitoneal approach either by open or laproscopic surgery.

The SURGIMESH®WN consists of non-absorbable synthetic mesh, made of nonknitted, non-woven fibers of polypropylene. SURGIMESH®WN mesh is supplied sterile and is available in rectangular and anatomic forms in order to meet the individual patient's surgical needs.

The provided text describes the regulatory clearance of a surgical mesh device, SURGIMESH®WN, and its substantial equivalence to predicate devices. However, the document does not contain specific acceptance criteria, detailed results from a study proving those criteria, or information on AI performance or ground truth generation methods.

The "Performance Data" section states: "Pre-clinical and clinical performance testing was conducted. Tissue integration, biocompatibility, product structure, and final product specifications were all tested, in addition to two clinical studies that were performed. In all instances, the SURGIMESH®WN functioned as intended and the results observed were as expected."

This general statement indicates that studies were performed and that the device met its intended function, but it lacks the quantitative details required to fill out the requested table and answer the specific questions about acceptance criteria and study design.

Therefore, most of the requested information cannot be extracted from the provided text.

Here's what can be answered based on the provided document:

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

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

• Sample Size for Test Set: Not specified in the provided text. The text mentions "two clinical studies," but no sample sizes are given for these studies.
• Data Provenance: Not specified in the provided text (e.g., country of origin, retrospective or prospective).

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 the device is a surgical mesh, not an AI device requiring expert ground truth for image interpretation or similar tasks. The evaluations would typically be based on objective measures, clinical outcomes, and histological analysis rather than expert consensus on interpretation.

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

• Not applicable/Not specified. This typically applies to studies requiring expert review or interpretation, which is not described for this device.

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. This is not an AI device.

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

• No. This is not an AI device.

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

• Based on the description of "Tissue integration, biocompatibility, product structure, and final product specifications," the ground truth likely involved:
  • Histopathology/Pathology: For tissue integration and biocompatibility (e.g., microscopic examination of tissue response).
  • Physical and Mechanical Testing: For product structure and final product specifications (e.g., tensile strength testing, pore size measurement).
  • Clinical Outcomes Data: For the "two clinical studies" (e.g., hernia recurrence, complications, patient satisfaction).

8. The sample size for the training set

• Not applicable. This is not an AI device that uses a "training set" in the machine learning sense. The "training set" in the context of a medical device development would refer to data used for initial R&D and optimization, which is not detailed here.

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

• Not applicable as this is not an AI device.

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