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Aongen™ Dental is intended for use in dental surgical procedures as a resorbable material for open wounds to aid in wound healing post surgery.

Aongen™ Dental Collagen Matrix is a white, resorbable matrix manufactured from porcine type I collagen and glycosaminoglycan. The function of glycosaminoglycan is water absorption. The device is supplied sterile and for single use only. Aongen™ Dental Collagen Matrix functions in a manner similar to the predicates.

The provided text describes the 510(k) summary for the Aongen™ Dental Collagen Matrix. It outlines the device description, indications for use, and a summary of tests conducted to demonstrate biocompatibility and performance. However, this document does not contain information typically associated with acceptance criteria or study details for a device whose performance is measured in terms of metrics like sensitivity, specificity, or accuracy, which would be relevant for software or diagnostic devices.

The Aongen™ Dental Collagen Matrix is a physical medical device (resorbable matrix for dental surgical procedures) and its evaluation focuses on biocompatibility and substantial equivalence to predicate devices, rather than performance metrics of an AI or diagnostic algorithm.

Therefore, many of the requested categories (e.g., sample size for test set, number of experts, MRMC studies, standalone performance, training set size, ground truth for training set) are not applicable to the type of device and study described in this 510(k) summary.

Here's the information that can be extracted and a clear indication of what is not applicable based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

For this type of device (collagen matrix), acceptance criteria are typically related to safety profiles like biocompatibility and sterility, rather than analytical performance metrics common in AI or diagnostic systems. The "reported device performance" here refers to meeting these safety standards.

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

This is not applicable as the device is a physical collagen matrix, not a software or AI device requiring a "test set" in the context of data or image analysis. The tests performed are laboratory-based biocompatibility studies, which would use biological samples or animal models, but the specific "sample sizes" (e.g., number of cells, animals) for each test are not detailed in this summary document. Data provenance as "country of origin" or "retrospective/prospective" is not relevant here.

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

Not applicable. This concept is relevant for algorithm-based devices where human experts define ground truth for training/testing data. For a physical device, testing involves standardized laboratory assays.

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

Not applicable. Adjudication methods are typically used for resolving disagreements in expert annotations for diagnostic or AI algorithm testing.

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. MRMC studies are designed for evaluating diagnostic devices/AI where human readers interpret cases. This device is a surgical material.

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

Not applicable. This device is not an algorithm.

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

For the biocompatibility tests, the "ground truth" is defined by the established scientific protocols and standards for each specific test (e.g., "non-cytotoxic," "not mutagenic," "sterile"). These are objective laboratory measurements against predefined thresholds, not expert consensus or pathology in a diagnostic sense.

8. The sample size for the training set

Not applicable. This device does not involve a "training set" as it's not an AI or machine learning model.

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

Not applicable. See point 8.

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