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Once finalized into a suitable design, the n!ce™ glass-ceramic blocks are indicated for use as inlays, onlays, veneers, partial crowns and crowns.

Straumann® n!ce™ glass ceramic is a proprietary lithium disilicate (Li2O-SiO2) glass ceramic material intended to be milled to produce prosthetic restorations for natural and endosseous dental implant abutment borne teeth. The material is suitable for use in inlays, onlays, veneers, copings and monolithic crown restorations. The mandrel bonded to the blocks is compatible with Amann Girrbach milling equipment.

This document is a 510(k) premarket notification for a dental ceramic material, "N!ce Glass Ceramic Blocks For Amann Girrbach." The information provided does not pertain to a medical device powered by Artificial Intelligence (AI). Therefore, the questions regarding acceptance criteria and study designs that are relevant to AI-powered devices (such as sample size, expert ground truth, MRMC studies, standalone performance, and training sets) are not applicable.

The document describes a physical medical device (dental blocks) and its laboratory performance testing against established ISO standards for ceramic materials. It focuses on demonstrating substantial equivalence to a predicate device, which is a common regulatory pathway for non-AI medical devices.

Here's how the provided information relates to the general concept of "acceptance criteria" for this specific device:

1. Table of Acceptance Criteria and Reported Device Performance (as relevant for a non-AI device):

Study Proving Device Meets Acceptance Criteria (as relevant for a non-AI device):

The "study" proving the device meets acceptance criteria refers to performance testing conducted according to recognized international standards.

2. Sample size used for the test set and the data provenance:

• The document refers to "test data" and "product performance testing" according to ISO standards. For materials testing, the "sample size" would typically refer to the number of physical blocks or specimens tested for each characteristic (e.g., flexural strength, chemical solubility). The specific numerical sample sizes for each test are not detailed in this summary.
• Data Provenance: Not explicitly stated, but typical for medical device manufacturers, this testing would be conducted within their own labs or by accredited third-party labs, adhering to Good Laboratory Practice (GLP) or similar quality systems. The data is retrospective in the sense that the testing was completed before the 510(k) submission. No country of origin for the data is specified, but the manufacturer is based in Switzerland (Institut Straumann AG) and the US (Straumann USA, LLC).

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 question is not applicable as the device is a physical material, not an AI algorithm that diagnoses or interprets data. "Ground truth" in this context refers to the measured physical properties of the material, which are determined by standardized laboratory methods and equipment, not by human expert interpretation.

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

• This question is not applicable. Adjudication methods like 2+1 or 3+1 are used in studies involving human readers or interpretations, especially for establishing ground truth in AI/ML validation studies. For material properties, the "truth" is determined by direct measurement following a validated test methodology.

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:

• This question is not applicable. MRMC studies are specific to evaluating the clinical performance of diagnostic or AI-assisted devices where human interpretation is involved. This device is a material used for restorations.

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

• This question is not applicable. A "standalone" performance evaluation refers to an AI algorithm's performance without human interaction. This device is a physical product, 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 objective, quantitative measurements of its physical and chemical properties (e.g., flexural strength in MPa, chemical solubility in µg/cm², CTE in K⁻¹) as outlined by the referenced ISO and ASTM standards. Biocompatibility is assessed based on standardized biological evaluation methods.

8. The sample size for the training set:

• This question is not applicable. Training sets are used in machine learning for developing AI algorithms. This device is a manufactured material, not an AI.

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

• This question is not applicable for the same reason as point 8.

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