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The DD contrast® veneering ceramics system is suitable for the esthetic individualization of monolithic or minimally reduced dental crown and bridge constructions made of zirconium dioxide or lithium disilicate.

DD contrast veneering ceramics are dental ceramics for the esthetic individualization of crowns and bridges made from zirconia or lithium disilicate. The DD contrast group consists of various ceramic veneering pastes (already pre-mixed) for coloring, texturing and glazing as well as liquids for adjusting the textures of the paste variants. The DD contrast products are applied to the sintered zirconium dioxide or lithium disilicate restoration and fired in a standard furnace for veneering ceramics. Due to the different consistencies, the pastes can be used to achieve thin shade and glaze layers as well as structure-building areas on the crowns. The veneering ceramics are of type I, class 1b) according to ISO 6872 (FDA Recognition Number 4-251).

The provided document is a 510(k) Premarket Notification for a dental device, specifically DD Contrast veneering ceramics. It does not describe a study involving an AI/ML algorithm or a device that would necessitate reporting on acceptance criteria for AI performance metrics like sensitivity, specificity, or AUC.

Instead, the document focuses on demonstrating substantial equivalence to an already legally marketed predicate device (InSync Ceramic System / MiYO Esthetic System Kit) based on non-clinical tests evaluating material properties and biocompatibility.

Here's an analysis of the provided information, framed to address the prompt's questions where applicable, but highlighting that AI-specific criteria are not relevant to this document:

1. A table of acceptance criteria and the reported device performance

The acceptance criteria are established by recognized international standards for dental ceramics and biocompatibility. The "reported device performance" demonstrates compliance with these standards.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document does not specify a "test set sample size" in the context of an AI/ML model. The testing here refers to material characterization and biocompatibility. The provenance is likely from Dental Direkt GmbH's own testing facilities or accredited labs in Germany, as the company is based in Germany. The nature of these tests (material properties) generally implies prospective testing performed on manufactured samples.

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 is not applicable to the provided document. "Ground truth" in the context of this submission refers to the established scientific and regulatory standards for dental materials, not expert consensus on diagnostic images.

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

This is not applicable. Adjudication methods are relevant for subjective evaluations, typically in clinical readings or when establishing ground truth from multiple human experts for AI model training/testing. The tests performed here (biocompatibility, flexural strength, material composition) are objective and quantitative measurements.

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

An MRMC study is not mentioned and is not relevant to this device. This device is a dental ceramic material, not an AI-assisted diagnostic tool.

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

This is not applicable as the device is a physical dental material and not an algorithm.

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

The "ground truth" for this device's performance is established by recognized international standards for dental materials and biocompatibility (e.g., ISO 10993-1, ISO 7405, ISO 6872). The device is evaluated against the parameters and thresholds defined in these standards.

8. The sample size for the training set

This is not applicable. There is no AI/ML algorithm or training set discussed in this document.

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

This is not applicable as there is no AI/ML algorithm or training set discussed.

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