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The PARIETEX™ OPTIMIZED COMPOSITE MESH is used for the reinforcement of tissues during surgical repair. It is indicated for the treatment of incisional hernias, abdominal wall repair and parietal (i.e. pertaining to the walls) reinforcement of tissues. The non-absorbable three-dimensional polyester mesh provides long term reinforcement of soft tissues. On the opposite side, the absorbable hydrophilic film minimizes tissue attachment to the mesh in case of direct contact with the viscera.

PARIETEX™ Optimized Composite Mesh is available in rectangular and round shape. This device is made out of a threedimensional multifilament polyester knit for wall reinforcement, covered with an absorbable, continuous and hydrophilic film on one of its sides. This film is made up of collagen from porcine origin and glycerol, and extends 5 mm over the edge of the reinforcement. One or many non-absorbable color monofilaments are tied to the three-dimensional mesh

The provided text describes the PARIETEX™ Optimized Composite Mesh, a surgical mesh, and its comparison to predicate devices, but it does not contain acceptance criteria or detailed study results for a device that uses AI or machine learning.

In general, information about acceptance criteria and comprehensive study results for AI/ML devices typically includes quantifiable metrics (e.g., sensitivity, specificity, AUC for diagnostic devices; accuracy, precision for other AI applications), thresholds for these metrics, and detailed breakdowns of how these metrics were achieved in various test sets.

The document discusses "performance data" which refers to bench testing and in-vivo animal model testing for the surgical mesh in question, focusing on mechanical properties and reduction of visceral attachment compared to a predicate device. This is a medical device, but not one that employs artificial intelligence or machine learning for diagnostic or predictive purposes, which is what the prompt seems to be geared towards.

Therefore, I cannot fulfill the request as formatted, as the input document does not provide information relevant to AI/ML device performance and acceptance criteria.

If the request is for the acceptance criteria and study general to this surgical mesh (and not specific to AI/ML):

Based on the provided text, here's what can be inferred about the acceptance criteria and studies (non-AI/ML specific):

1. Table of Acceptance Criteria and Reported Device Performance

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

• Mechanical Testing (Bench): Not specified. Likely involves a sufficient number of devices for statistical analysis of mechanical properties.
• In-vivo performance testing (Animal Model): Not specified.
  • Data Provenance: The document does not specify the country of origin for the animal study, but the submitter is based in France. The study is prospective in the sense that it evaluates the performance of the new mesh design.

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 type of information (expert review for ground truth) is not applicable to the mechanical and animal model studies described for this surgical mesh. The "ground truth" for mechanical properties is typically established by engineering standards and measurements, while for the animal study, it would be direct observation and pathological assessment of visceral attachment by veterinary pathologists or researchers. No specific number or qualifications of such experts are mentioned.

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

• Not applicable for the types of studies described (bench testing, animal model). Adjudication methods are more commonly used in clinical trials or image-based diagnostic studies involving human interpretation.

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 device is a surgical mesh, not an AI-assisted diagnostic tool.

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

• No, this is not an AI algorithm. Therefore, standalone performance (in the context of AI) is not applicable.

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

• Bench Testing: Mechanical measurements against engineering standards.
• In-vivo Performance Testing: Direct histological or gross pathological assessment of visceral attachment in an animal model.

8. The sample size for the training set:

• Not applicable. This is not an AI/ML device that requires a 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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