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OptiBond FL is a multi-purpose bonding agent designed to be used in the following situations: composite to enamel and/or dentin, composite repair, porcelain repair, composite to metal, bonding composite core build-up materials, and veneers, onlays, and inlays.

The device is a multi-purpose bonding agent designed to be used in the following situations: composite to enamel and/or dentin, composite repair, composite to metal, bonding composite core build-up materials, and veneers, onlays, and inlays.

This document is a 510(k) summary for a dental resin bonding agent, not a study report for a digital health device or AI/ML algorithm. Therefore, it does not contain the information required to answer your specific questions regarding acceptance criteria and a study proving device performance in the context of an AI/ML device.

Here's why and what information is available:

• Device Type: The device described, OptiBond FL, is a Resin Tooth Bonding Agent (a physical dental material), not a digital health device or an AI/ML algorithm.
• Approval Type: This is a 510(k) premarket notification, which establishes substantial equivalence to a legally marketed predicate device, rather than proving performance against specific acceptance criteria through a clinical trial or algorithm validation study in the way an AI/ML device would.
• Focus of Document: The document focuses on the device description, intended use, and substantial equivalence to a predicate device (OptiBond Solo Plus 2). It confirms that the FDA reviewed the submission and found the device substantially equivalent.

Therefore, I cannot extract the following information because it is not present in the provided text:

1. A table of acceptance criteria and the reported device performance.
2. Sample size used for the test set and the data provenance.
3. Number of experts used to establish the ground truth for the test set and their qualifications.
4. Adjudication method for the test set.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, or the effect size of human readers improving with AI vs without AI assistance.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc).
8. The sample size for the training set.
9. How the ground truth for the training set was established.

This document is a regulatory approval notice for a medical device, not a performance study report for an AI/ML system.

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