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Duatene™ Bilayer Mesh is intended for the reinforcement of abdominal wall soft tissue where a weakness exists. It is indicated for the open repair of groin hernia defects.

Duatene™ Bilayer Mesh is designed to be placed in an extraperitoneal site by open approach. Duatene™ Bilayer Mesh is a three-dimensional device made out of polypropylene monofilament textile knitted in one-piece. The three-dimensional textile is composed of: a posterior textile layer which is circular or elliptic in shape (for preperitoneal placement); an anterior textile layer which is oblong in shape (for onlay placement). The two textile layers are designed with differentiated knitting patterns, adapted to the function of each layer. The two layers are linked by crossing threads from both textiles. The mesh is designed to be placed over the groin region to ensure long term reinforcement of soft tissues.

The provided text is a 510(k) summary for the Duatene™ Bilayer Mesh, a surgical mesh. This document describes the device, its intended use, and the studies conducted to demonstrate its substantial equivalence to predicate devices, rather than a study proving a device meets specific acceptance criteria for an AI/ML-based diagnostic or prognostic device.

Therefore, the document does not contain the information requested regarding acceptance criteria and a study that proves a device meets those criteria for an AI/ML-based device. The original request is geared towards describing the validation of an algorithmic device (AI/ML), which this submission for a new surgical mesh does not cover.

To address the specific points in the request, based on the provided text:

1. A table of acceptance criteria and the reported device performance: Not applicable. This document is about a material/surgical device, not an AI/ML performance. The "performance data" mentioned are for physical characteristics of the mesh (pore size, thickness, bursting strength, etc.) and biological compatibility, not AI/ML metrics like AUC, sensitivity, or specificity.
2. Sample size used for the test set and the data provenance: Not applicable. The "test set" here refers to the physical mesh samples used in bench tests or animal models, not a dataset of medical images or patient records for an AI/ML system. Data provenance is not specified in terms of patient origin.
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. Ground truth in this context would be laboratory measurements or pathological analysis from animal studies, not expert human interpretation for an AI/ML task.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. Adjudication of expert readers is relevant for AI/ML studies, not for the physical and biological testing of a surgical mesh.
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. There is no AI component involved; this is a traditional medical device submission.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. There is no algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): The ground truth for this device's performance would be established through laboratory measurements based on established standards (e.g., ISO Standard 10993-1 for biocompatibility, and "Guidance for the Preparation of a Premarket Notification Application of a Surgical Mesh" for mechanical properties), and histological evaluation in animal studies. It's not data based on expert consensus for diagnostic interpretation.
8. The sample size for the training set: Not applicable. There is no AI model to train.
9. How the ground truth for the training set was established: Not applicable.

The conclusion of the summary explicitly states: "This premarket submission did not rely on the assessment of clinical performance data to demonstrate substantial equivalence." This further indicates that the requested information for an AI/ML device's validation is not present. Instead, substantial equivalence was demonstrated through:

• In vitro (bench) tests comparing physical properties of the mesh to predicate devices.
• In vivo pre-clinical tests on representative animal models to evaluate tissue integration.
• Biocompatibility evaluation.
• Usability tests.

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