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1. Direct anterior and posterior restorations (including occlusal surfaces)
2. Base/liner under direct restorations
3. Core build-ups
4. Splinting
5. Indirect restorations including inlays, onlays and veneers
6. Restorations of deciduous teeth
7. Extended fissure sealing in molars and premolars
8. Repair of defects in porcelain restorations, enamel, and temporaries

REVEAL Bulk is a light-activated restorative composite, optimized to allow for simpler and faster posterior restorations. REVEAL Bulk is intended to combine superior levels of handling, depth of cure, and polishability to perform as an optimum functional and aesthetic bulk fill composite.

The provided document is a 510(k) summary for a dental restorative material called REVEAL Bulk. It does not describe a study involving an AI device or a comparative effectiveness study with human readers. Instead, it focuses on demonstrating substantial equivalence of REVEAL Bulk to an existing predicate device based on physical, mechanical, and biocompatibility properties.

Therefore, many of the requested categories for AI device studies, such as sample sizes for test sets, data provenance, expert ground truth establishment, adjudication methods, MRMC studies, standalone algorithm performance, and training set details, are not applicable to this document.

However, I can extract the relevant information regarding the acceptance criteria and performance of the REVEAL Bulk device for its intended use as a dental restorative material.

Acceptance Criteria and Reported Device Performance for REVEAL Bulk

1. Table of Acceptance Criteria and Reported Device Performance:

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

• The document does not specify the exact sample sizes (number of specimens) used for each physical/mechanical test.
• The data provenance is from laboratory testing conducted by Bisco, Inc. (the applicant) on their REVEAL Bulk device. The standards referenced (e.g., ISO 4049:2009, ISO 7405:2008, ISO 10993-1) are international standards for dental materials and biocompatibility.

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

• This question is not applicable as the "ground truth" for this medical device submission relies on established international standards (e.g., ISO for physical properties) and scientific principles for material characterization, rather than human expert consensus on diagnostic images or clinical outcomes. The performance is measured against these objective standards and comparison to a legally marketed predicate device.

4. Adjudication method for the test set:

• This question is not applicable as there is no human interpretation or adjudication involved in the objective physical and mechanical property testing of a dental restorative material. The results are quantitative measurements against defined criteria.

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. The submission is for a dental restorative material, not an AI device. No MRMC study was conducted.

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

• This question is not applicable. The submission is for a dental restorative material, not an AI algorithm.

7. The type of ground truth used:

• The "ground truth" for this device's performance is based on established international standards and predicate device performance. For example:
  • ISO 4049:2009 for polymer-based restorative materials, specifying test methods and requirements for properties like flexural strength, radiopacity, water sorption, solubility, and depth of cure.
  • ISO 7405:2008 and ISO 10993-1 for biological evaluation of medical devices (biocompatibility).
  • Equivalence to a legally marketed predicate device (Filtek Bulk Fill K141081) for other properties like diametral tensile strength, volumetric shrinkage, compressive strength, wear, polish retention, surface hardness, and cusp deflection.

8. The sample size for the training set:

• This question is not applicable. This is not an AI device or a machine learning model, so there is no "training set."

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

• This question is not applicable. There is no training set for this type of device submission.

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