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• -Cementation of metal-based inlays, onlays, crowns and bridges
• -Cementation of resin inlays, onlays, crowns and bridges
• Cementation of all ceramic inlays -
• -Cementation of high strength (e.g. zirconia based, lithium disilicate) ceramic onlays, crowns and bridges
• Cementation of metal, ceramic and fiber posts -

GC FujiCEM 2 (Improved) is a resin modified glass ionomer luting cement. The device is composed of the two pastes, Paste A and Paste B, packaged in an automix dual barrel syringe.

The provided document is a 510(k) summary for a dental cement (GC FujiCEM 2 (Improved)). It focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance testing. It does not contain information about software or AI-based devices. Therefore, a table of acceptance criteria and reported device performance for an AI/software device cannot be generated from this text.

The document discusses the physical and chemical properties of the dental cement through performance bench tests and biocompatibility assessments against established ISO standards.

Here's an analysis of the available information regarding the device's evaluation, although it's for a physical dental cement product, not an AI device:

1. Table of Acceptance Criteria and Reported Device Performance (Non-AI/Software):

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

• Not applicable for AI/Software. The document describes non-clinical bench testing. The sample sizes for each specific test (e.g., number of specimens for flexural strength) are not explicitly stated within this summary. Data provenance is implied to be from the manufacturer's own testing.

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

• Not applicable for AI/Software. Ground truth for dental cement properties is established through standardized physical and chemical testing methods, not expert consensus in the way it's used for AI diagnostic outputs.

4. Adjudication method for the test set:

• Not applicable for AI/Software. The performance criteria for the dental cement are objective metrics measured by equipment and standardized procedures, not subjective interpretations requiring adjudication.

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. This document is for a dental cement, not an AI or software device.

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

• Not applicable. This is a physical medical device (dental cement), not an algorithm or software. No standalone algorithm performance was assessed.

7. The type of ground truth used:

• For biocompatibility: Ground truth is defined by the criteria and methods outlined in ISO 10993 series standards.
• For performance bench tests: Ground truth is defined by the specifications and methods in ISO 9917-2: 2017 (Dentistry - Water-based cements - Part 2: Resin-modified cements) and the company's internal standards. These are objective, measurable physical and chemical properties.

8. The sample size for the training set:

• Not applicable. This is a physical medical device. There is no training set in the context of AI/machine learning.

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

• Not applicable. See point 8.

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1. Cementation of all types of all ceramic, resin and metal-based inlays, onlays, crowns and bridges.
2. Cementation of metal, ceramic, fiber posts, and cast post and cores.

GAM-200 is a dual-cured self-adhesive resin cement. The mixed cement is hardened through the polymerization activated by redox or photo initiator system. The adhesiveness to tooth structure develops with phosphoric ester monomers which form ionic bonds with calcium ions. The acidic monomers also can polymerize with frame-forming monomers. The components consists of Paste A and B, which are filled in a one-body syringe. Both pastes are automixed with a mixing tip and directly applied to restorations or the prepared cavity.

The provided text describes a 510(k) premarket notification for a dental cement device, GAM-200. This type of submission is for demonstrating substantial equivalence to a predicate device, not for proving effectiveness through clinical studies that would involve acceptance criteria and performance metrics in the way a medical AI/CAD device would.

Therefore, the information requested in your prompt (e.g., acceptance criteria, sample size for test/training set, number of experts, adjudication methods, MRMC study, standalone performance, ground truth types) is not applicable to this type of device and submission.

The document discusses the technical characteristics and equivalence of the GAM-200 dental cement to predicate devices based on material properties and in vitro tests, not on clinical performance in diagnosing or assisting with interpretation in a human-in-the-loop setting.

However, I can extract the relevant information that is present in the document related to how the device's characteristics were assessed against established standards and predicate devices:

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample size used for the test set and the data provenance: Not applicable. The evaluations are primarily in vitro material science tests against standards and predicate devices, not clinical trials with human subjects.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for material properties is established through standardized testing procedures.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.

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. This is a dental cement, not an AI/CAD system for image interpretation.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): The "ground truth" here is based on established international standards (ISO 4049: 2009, ISO 11405: 2003) and laboratory test results for material properties and biological evaluation (cytotoxicity via MTT Assay). The comparison is against the performance of legally marketed predicate devices.

8. The sample size for the training set: Not applicable. There is no machine learning "training set" for this device.

9. How the ground truth for the training set was established: Not applicable.

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