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Admira is a universal, light-cured restorative material. Admira is intended to be used for the following types of restorations in both anterior and posterior teeth:

• · class I-V fillings
• reconstruction of traumatically affected anteriors
• facetting of discolored anteriors
• · correction of shape and shade to improve asthetic appearance
• · locking or splinting of loose anteriors
• · repairing veneers
• · core build-up under crowns
• · composite inlays

Admira is a universal, light-cured restorative material based on 3dimensionally stabilized, inorganic/organic polymers (ormocers). Patentprotected ormocer chemistry provides exceptional strength, abrasion resistance, and hard tissue adhesion, while making Admira easy and fast to use. Admira further combines the benefits of silicate glass with the strength, durability, and cosmetic advantages of composites. Admira is suitable for restorations in both anterior and posterior teeth. Admira is used with Admira Bond, a bonding agent that has been specifically designed for the total-etch technique.

Admira is available in both 4gm syringes, and as pre-dosed (0.25gm) Admira Caps (composite application system) for direct intra-oral application. Admira is available in ten tooth shades.

This document is a 510(k) summary for the dental restorative material ADMIRA®. It focuses on demonstrating substantial equivalence to predicate devices rather than directly presenting acceptance criteria and a study proving those criteria are met for a new AI/ML device. Therefore, I cannot extract the requested information from the provided text.

Here is an explanation of why the information is not present in the document and what each bullet point would typically refer to in the context of an AI/ML device submission:

1. A table of acceptance criteria and the reported device performance: This document does not contain acceptance criteria for the ADMIRA® device in the format typically seen for AI/ML performance. It describes the physical and chemical properties and intended use. For an AI/ML device, this table would specify metrics like sensitivity, specificity, AUC, or other relevant performance measures (e.g., accuracy for image segmentation) and the statistical thresholds considered "acceptable."
2. Sample size used for the test set and the data provenance: The document does not describe a test set or its provenance for ADMIRA®. For an AI/ML device, this would detail the number of cases/images used to evaluate the final model, where this data came from (e.g., specific hospitals, geographic regions), and if it was collected retrospectively or prospectively.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable to this document. For an AI/ML device, this would specify how many human experts (e.g., Board-certified radiologists) reviewed the test data to create the "correct" labels (ground truth) and their experience level.
4. Adjudication method for the test set: Not applicable. For an AI/ML device, this would describe the process used to resolve disagreements among experts when establishing ground truth (e.g., using a super-reader, majority vote, or separate adjudication panel).
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. An MRMC study evaluates the performance of human readers with and without the AI device, measuring the improvement in reader accuracy, efficiency, or other metrics. This document does not describe such a study.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable. A standalone study for an AI/ML device assesses the algorithm's performance on its own, without any human intervention or interpretation. While the document mentions "preclinical performance studies," it does not provide details of how these demonstrate "standalone" performance in the context of an AI/ML diagnostic tool.
7. The type of ground truth used: Not applicable. For an AI/ML device, this would specify if the ground truth was derived from expert consensus, histopathology, surgical findings, long-term patient outcomes, or a combination.
8. The sample size for the training set: Not applicable. For an AI/ML device, this would be the number of cases/images used to train the algorithm.
9. How the ground truth for the training set was established: Not applicable. For an AI/ML device, this would describe the methods used to label the data that the algorithm learns from, similar to point 7 but specifically for the training data.

In summary, the provided document is a 510(k) summary for a dental material, which is a very different type of medical device from an AI/ML diagnostic tool. The regulatory requirements and the information presented are therefore not aligned with what would be expected for an AI/ML device submission.

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